Delving into Large Language Models for Effective Time-Series Anomaly Detection
Authors: Junwoo Park
Kyudan Jung
Dohyun Lee
Hyuck Lee
Daehoon Gwak
ChaeHun Park
Jaegul Choo
Jaewoong Cho
Venue: NeurIPS 2025 poster ()
Keywords: large language model
time series
anomaly detection
Primary area: applications

Abstract: Recent efforts to apply Large Language Models (LLMs) to time-series anomaly detection (TSAD) have yielded limited success, often performing worse than even simple methods. While prior work has focused solely on downstream performance evaluation, the fundamental question—why do LLMs struggle with TSAD?—has remained largely unexplored. In this paper, we present an in-depth analysis that identifies two core challenges in understanding complex temporal dynamics and accurately localizing anomalies. To address these challenges, we propose a simple yet effective method that combines statistical decomposition with index-aware prompting. Our method outperforms 21 existing prompting strategies on the AnomLLM benchmark, achieving up to a 66.6\% improvement in F1 score. We further compare LLMs with 16 non-LLM baselines on the TSB-AD benchmark, highlighting scenarios where LLMs offer unique advantages via contextual reasoning. Our findings provide empirical insights into how and when LLMs can be effective for TSAD. The code is publicly available at: https://github.com/junwoopark92/LLM-TSAD

Delving into Large Language Models for Effective Time-Series Anomaly Detection
Junwoo Park Kyudan Jung 1.2 Dohyun 1:2 Hyuck Lee", Daehoon Gwak', ChacHun Park Jaegul Choo Jacwoong Cho?
KKAIST AI
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Abstract
Recent efforts apply Large Language Models (LLMs) to time-series anomaly detection (TSAD) have yielded limited success, often performing worse than even simple methods. While prior work has focused solely on downstream performance evaluation, the fundamental question-why do LLMs struggle with TSAD?-has remained largely unexplored. In this paper, we present an in-depth analysis that identifies two challenges in understanding complex temporal dynamics and curately localizing anomalies. To address these challenges, we propose simple yet effective method that combines statistical decomposition with index-aware prompt- ing. Our method outperforms 21 existing prompting strategies on the AnomI_.M benchmark, achieving up 66.6% improvement in F1 score. We further compare LLMs with 16 non-LLM baselines on the TSB-AD benchmark, highlighting sce- narios where LLMs offer unique advantages via contextual reasoning. Our findings provide empirical insights into how and when LLMs can be effective for TSAD. The code is publicly available at: htttps://gitie htt.stttu.ooooo//aeeeemmoetettet http://joooooook///11aa..
1 Introduction
Large language models (LLMs) have shown impressive zero-shot numerical reasoning capabilities across various tasks, including math problem-solving 28] 41), tabular analysis [99
27 55 66], and
time series forecasting [26] [54] 68). These numerical reasoning tasks typically require interpreting logical operations.
numbers embedded in natural language prompt, performing arithmetic and accurately understanding numerical relationships within their domain context. Time series anomaly detection (TSAD) similarly involves these capabilities, as it requíres interpreting numerical deviations and recognizing temporal patterns within time series. Motivated by these similarities. recent studies their strengths in handling diverse input formats and leveraging
20 45 79
83
87 89 have shown the potential of LLMs for TSAD, highlighting contexts.
However, while demonstrating the potential of LLMs for TSAD, these studies simultaneously reveal significant limitations. Specifically, LLMs frequently exhibit false positive detections when time series are presented in textual form, often misclassifying normal fluctuations or pervasive background noise as anomalies 4 20]. As an alternative, graphical plots have been used; however, LLMs often only identify prominent spikes or visually salient. anomalies, missing more nuanced anomalies These failure cases have led to a pessimistic view of LLMss capabilities at TSAD. However, prior work has focused predominantly on evaluating downstream detection accuracy, without addressing the fundamental question: why do LLMs struggle with TSAD? The underlying reasons, whether due to input representations, temporal modeling, or prompting strategies, remain unclear.
[7 89].
To systematically investigate these unresolved questions, we begin by decomposing the TSAD problem into two essential subtasks: (1) distinguishing between normal and anomalous patterns (time-series understanding). and (2) identifying the precise anomaly intervals (anomaly localization).
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Unlike prior works that treat TSAD as a monolithic task [4 20 79 89], this decomposition allows us isolate and diagnose the distinct failure modes of LoMs.For the first subtask, our preliminary analysis reveals that LLMs often miss subtle anomalies when complex normal patterns, such as overlapping trend and seasonality, obscure their presence. For the second subtask, we observe that widely used prompts implicitly require LLMs to count sequence tokens to pinpoint anomaly positions-a capability that LLMs consistently struggle with 78 85). This task decomposition thus surfaces fine-grained limitations of LLMs that remain hidden under aggregate performance metries.
These observations directly inform our solution, which explicitly targets both limitations. To address the difficulty of detecting anomalies obscured by complex normal patterns, we apply statistical decomposition to remove only the seasonal component (i.e.. de-seasonalization) Since seasonal patterns exhibit consistent, repeating structure, they are typically treated as normal rather than anomalous. Removing seasonal patterns enhances the visibility of subtle anomalies that are concealed within complex normal dynamies, thus facilitating accurate detection by LLMss. To overcome LLMs' struggles with position reasoning via token counting, we design index-aware prompting that explicitly embeds positional cues in the input. This approach highlights the often-overlooked utility of time-series text, in contrast to prior methods that predominantly rely on visual inputs.
As a result, on the AnomLLM benchmark [89], our method surpasses all 21 existing prompting strategies, boosting FI scores by an average of 39.9% and up to 66.6% across both open- open-source and proprictary LLMs. Ablation studies further validate the critical role of both statistical decomposition and explicit indexing. Finally, building on these improvements, we revisit a question often left unaddressed in earlier work: L.MS' practical values in TSAD compared to conventional numerical methods. We benchmark our method against 16 non-LLM methods from the TSB-AD suite [46], comparing zero-shot accuracy and inference efficiency. We also explore scenarios where the contex- tual reasoning capabilities of LLMs offer practical benefits, such as selectively excluding known or explainable anomalies by domain-specific contexts.
Our main contributions are as follows:
We present comprehensive analysis of the fundamental limitations of LLMs in TSAD, explicitly identifying the challenges in understanding temporal patterns and accurately localizing anomalies.
To address these limitations, we propose a simple yet effective method that combines statistical decomposition with index-aware prompting, significantly improving the average Fl score by up to 66.6% compared to existing LLM- -aadd-beedd-- TSAD methods.
Based on the improved performance, we evaluate the practical justification for employing LLMs in TSAD by comparing them with diverse non-LLM methods, highlighting specific scenarios in which the contextual reasoning capabilities of LLMs offer tangible advantages.
2 Analysis: why do LLMs struggle with TSAD?
Throughout our study, we build on the experimental setup proposed in AnomLLM 89 which systematically categorizes recent prompt designs and anomaly types. For clarity and due to space constraints, we provide detailed descriptions of representative TSAD methods and other LLM--based methods in the related work section in Appendix A.I
Task Setup. In the LLM-based TSAD, an input time series Xt- (xt- into a comma-separated list of normalized values (time-series text) or a simple line plot (time-series image). These representations are then fed into an LLM, guided by natural language instructions such as: "Identify all contiguous intervals [a,, b.] that exhibit anomalous behavior." The LLM generates a free-form textual response, which is post-processed to extract interval predictions of the form {(1,,1)) (ag, by).... (aN,, bb,)): J. where each (a;, 6;) denotes anomaly span. This formulation differs from conventional TSAD methods, which compute scalar anomaly score for each timestamp followed by thresholding.
) is first converted
Analysis of common failure cases. We begin with an analysis of AnomLLM failure cases. Figure] [ shows two cases that both exhibit an Fl score of zero but reflect distinct types of failure in the anomaly detection process: (a) the L.LM fails to detect any anomaly, despite the presence of trend shift anomaly; (b) the LLM produces prediction; however, the predicted anomaly interval does

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(a) Time-series Understanding Failure
(b) A nomaly Localization Failure
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Two representative failure cases of LLM-based TSAD. Although both cases result in an zero Fl score, the reasoning of LLM outputs reveal that the causes stem from different limitations: one in understanding the time series and the other in localization ability.
Figure 1:
not overlap with the ground truth, resulting in a zero score. Interestingly, when asked to explain its reasoning case (b), the model correctly attributes the anomaly to a sudden increase, indicating a successful understanding of the time series pattern. This suggests that the failure in case (b) lies primarily in the localization step, not in understanding. On the other hand, in case (a), the model concludes that there are no anomalies at all, implying a fundamental failure in recognizing the anomaly the first place.
These examples highlight two key challenges LLM-based zero-shot anomaly detection: (l) understanding time-series dynamics, and (2) accurately localizing the anomaly intervals. To isolate these two types of failure, we conducted two experiments: instance-level TSAD /Section[..1 which focuses the understanding failures (a) and TSAD with Ground Truth (GT) anomalies (Section 2.2) which targets the localization failures (b).
2.1 Evaluating LLMs* time-series understanding capability
Trend shift
Frequency change
Contextual point
Out of range
Buadite (Corotent Cassifie)
0.5
Manningfll Gaia
0.5
F1 soore
06-
Add time series statistics
CoT variants
In Conten (1sbot)
Image
Text
Figure 2: Instance-level TSAD results of 21 prompts (A-U), including two sequence representation (image: A-H and text: I-U), COT (A,E,L,O-)), and In-Context (A-D, I-J) strategies (details in Table on four anomaly types to estimate understanding capability of LLMs. Even with these prompts, LLMs still struggle to detect anomalies such as trend shifts and frequency changes.
In TSAD, final performance is ultimately calculated by either sequence-length binary labels or interval predictions, inevitably depending on localization capability. Thus, we relax the original problem into a simpler formulation, deciding only whether any anomaly exists in the entire sequence without precise localization, which we refer as instance-level anomaly detection. This binary detection task is inherently easier than identifying multiple anomalous intervals in the sequence. Therefore, if model fails to even detect the presence of an anomaly, it clearly lacks the time-series understanding necessary for TSAD. Experimental details including the task prompt be found in Appendix D.I
Figure 2 illustrates the varying degrees of time-series understanding exhibited by LLMs across different anomaly types. For trend and frequency anomalies- which involve subtle and intertwined changes that closely resemble normal patterns LLMS perform only marginally better, or even worse, than a trivial baseline that always predicts the same label (normal or abnormal) regardless of the input. In contrast, for point and range anomalies, LLMs using sequence image prompts (A-H) achieve near-perfect accuracy, unlike those relying on the time-series text. Overall, despite employing prompt strategies such as Chain of Thought (CoT) and various in-context exemplars- effectivee in many NL.P tasks-LLMs exhibit limited capability understand temporal dynamics.
3

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(a) Fl of L. Ms with GT labels
(b) F1 of anomaly types with GTT labels
(c) Anomaly statisties
p00
Tonl
Totel (Qwen2.5)
TrandiFaaluee 2.7)
Trand
2M4
locdloatio
GPT-lo-misi-
X-
Frog
15A
13.
734
Point (36,1)
Ge-
433
Point
*Range (68.5)
Qwen2.5
Kange-
Frog (70.9
WT
Isceased GT
Rensliiin Fallue
FI Sore
60
Arg. AofArmlaiio
Figure 3: TSAD results with GT anomaly labels to estimate localization failure. (a) shows the F1 improvement by GT labels for each model, (b) shows the improvement by GT labels for each anomaly type, and (c) illustrates the relationship between the number and length anomalies per type and the remaining failure.
2.2 Evaluating LLMs' time-series localization capability
To determine whether the limited performance of LLMs originates from poor localization, design a localization-only experiment that explicitly removes the need for time-series understanding. Specifically, we highlight GT anomaly regions within the time-series image (e.g., (a) and (b) in Figure T). Apart from this visual cue, the task setup remains identical to the original TSAD setting: the model must return anomaly intervals along the x-axis. For clarity, we use time-series images here, but Section 4.2 also evaluates localization on time-series text. The prompt and additional details for this experiment are provided in the Appendix D.2
In this configuration, the location of anomalies is explicitly given within the input image, and the model is only required to translate this information into precise coordinate predictions. If localization were not a bottleneck, we would expect that LLMs could achieve near sear-perfect scores.
However, as shown in Figure 3 a). although all models benefit from this GT GT-provided setting, the performance gains remain limited. This suggests that the bottleneck lies not only in identifying where anomalies occur but also in precisely localizing those regions. In other words, even when L.Mss are given highlighted regions, they often struggle to translate them into accurate output, indicating core limitation in localization capability.
Beyond the overall performance plateau, closer look individual anomaly types reveals further insights into localization difficulty. As shown in Figure 3(b), trend and frequency anomalies previously the most challenging for understanding (Figure 2 exhibit larger Fl improvements than point and range anomalies. This disparity suggests that localization difficulty is not uniform but is closely tied to the characteristics of each anomaly type. Figure 3e reinforces this interpretation: residual error rates correlate with the number and fragmentation of anomaly segments. Frequency anomalies, in particular, often consist of many scattered segments, making precise localization substantially harder.
Methodology
Removing Seasonality
Trad
Image plot
Index-aware Prompting
Seesunslly
Promet
Time-series Decumposition
Multi-modal LLM
2777, 2.41.
3.11, 3.25).
Input
Resihhal
2.00.
Prediction
ranges anomalies this time scries.
Domain specific
ACF
Contret
Figure 4: An overview of our proposed method to mitigate understanding and localization challenges.
From the two preceding experiments, we confirmed that the current performance limitations of LLMS TSAD stem from both the understanding and localization challenges. Understanding complex temporal structures has long been a central challenge in time-series analysis. A representative strategy decompose time series into structural components such as trend, seasonality, and residuals [5]. Existing non-LLM methods [53 74 75 have often embedded this decomposition internally within

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models through end-to-end learning pipelines, However, for LLMs with strong pretrained reasoning abilities, training them to perform decomposition may degrade their pretrained capabilities and introduce inefficiencies, particularly due to the need to decode all outputs as text compared to the use of numerical algorithms.
Motivated by recent advances in tool-augmented LLM approaches [30 56], we introduce a simple yet effective method that enhances LLM-bbsse TSAD by leveraging lightweight external tools for time-series decomposition. This design allows the LLM to focus on its core strength-reasonin over structured prompts-Wi while benefiting from accurate, preprocessed inputs, as illustrated Figure
3.1 De-seasonalization: supporting LLMs' understanding of time series
Pronounced seasonality often obscures subtle trend shifts and frequency changes, causing LLMs to overlook anomalies that become obvious once the seasonal cycle is removed. We therefore insert lightweight de-seasonalization module directly before the LLM-based detector:
Step 1: Period discovery: : Given an input time series 21,7, compute the sample autocorrelation 37 function (ACF) ý for lags k = =,.... [T/2] and identify the dominant period
p = arg max p(k:).
121
Step 2:
Additive decomposition: Using classic decomposition 5 12 with window length P",
decompose H, = 8t + 72 + Tt, where st seasonality, 7 the trend, and r. the residual.
Step 3:
Residual pass-through: Feed the de-seasonalized sequence मे 22. to the LLM as both a time-series text and image.
In this process, global autocorrelation is used to estimate the dominant period, enabling the decompo- sition to remove only stable, recurring seasonality, while preserving subtle deviations like trend or frequency changes for LLM-based detection.
From cost perspective. de-seasonalization can be efficiently implemented using a fast Fourier transform, reducing the complexity to O(T log T), and incurring negligible overhead relative LLM inference. More sophisticated methods, such as STL [58), Seasonal-HODMM. [40], and Prophet [69] can also be integrated with modest additional overhead and without altering the pipeline. Even this lightweight preprocessing step alone yields consistent improvements in F1 score across all evaluated L.MMs, as demonstrated in Table
Although de-seasonalization using an external algorithm clearly enhances performance, it raises two key questions. First, might LLMs already possess an implicit ability to perform such decomposition without the need for explicit tools or additional training? Second, in a truly tool-augmented framework, should not an LLM be able determine whether such preprocessing is necessary? These questions invite a closer examination of the boundary between what LLMs can do inherently and where external tools remain essential. We explore this boundary in depth Section 4.2
3.2 Index-aware prompting: challenges in positional reasoning over time-series text
The localization problem differs de- pending on whether the time series is presented as an image or text. When using time-series image alone, lim- ited index visibility along the x-axis constrains LLMs infer anomaly intervals from relative visual posi- tions. On the other hand, when using time-series text, existing AnomLLM prompts typically present only a list of numerical values without any explicit positional information. As a result, the LLM must implicitly perform counting task infer the positions of the anomalies. However, counting tokens semantic chunks is well-documented challenge for
Counting Errors across L.MSs
Counting Errors across Separater
Qwent. S S-VI--2R--la
Space
Gemini-1 1.5-Flash
Newlize
Figure 5: Counting performance of LLMs, quantified by Mean Absolute Error (MAE), across various sequence length intervals. (a) Comparison across LLMs. (b) Comparison across four separator tokens.
5

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To assess this limitation for the time-series domain, we designed simple task that requires counting the number of values in a numerical sequence. As shown in Figure both open- source and proprietary LLMs perform surprisingly poorly on this simple task, even when various separator tokens are used.
LLMs 185.
To alleviate the counting burden, we introduce index-aware prompting, in which each value in the input sequence is explicitly paired with its positional index. This design allows the LLM directly return the start and end indices of an anomalous segment once detected, eliminating the need for internal counting and effectively transforming a difficult counting task into a simple lookup. Moreover, since visualizing indices in time-series plots often leads to clutter and overlap, expressing positional information in textual form becomes more practical. This highlights the previously underappreciated utility of time-series text in TSAD.
While emphasize the use of index-aware prompting to improve anomaly localization, incorporation of index information is not entirely novel. Liu et al. [45] incorporated time-series text with indices. However, most prior works [20] 79 89 have not utilized index information, possibly due to concerns that longer and more complex prompts could negatively impact task accuracy. To better understand this trade-off, we revisit the issue through empirical evaluation in Section 4.2
Experiment
Datasets. We leverage the well-curated datasets introduced by AnomLLM [89). which include four representative anomaly types-point, range, trend, and frequency-with accurately annotated intervals. These datasets are designed to facilitate fine-grained evaluation under various anomaly conditions. To complement these controlled settings, we also incorporate the TSB-AD-U bench- mark [46]. which reflects unsupervised anomaly detection in more realistic time-series applications. Certain prompting methods 45| { incorporate domain-sspciif context that characterizes the type of anomaly being tested, whereas our method does not utilize any such context in these benchmarks. The dataset configurations follow the original benchmark and are summarized in Appendix [B.2]
Metric. A 89 uses affiliation metrics [31] as its main evaluation metric, as it offers the advantage of distance-based correction. However, this metric is less sensitive to localization errors and does not clearly differentiate between methods. To ensure a more rigorous evaluation, we therefore employ both the standard and the affiliation metrics.
Baselines. We perform experiments using four representative multi-modas LLMs, two of which are open-sourced: Qwen2.5- Swen2.5-VL-722--BB -L--VVL------L--- and proprietary: GPT-4o (and mini) and Gemini-1 1.5-Fashh We replicate the 21 prompt variants from the L.M benchmark [89 covering text vs. vision modalities, input formats, and reason- ing styles. For comparison with conventional TSAD models in Section
and two of which are
5
we also report results Sub-PCA (2), KMeans-
from 16 non-L.L. baselines such as MI. baselines (SR [60], IForest
44
AD 82], MatrixProfile [84). DL baselines (CNN [49, USAD
7 Anomaly Transformer (77).
OmniAnomaly (65). AutoEncoder [62). TimesNet (75], FITS 80)) Foundation Model (FM) baselines
(Chronos 6), TimesFM [18] Lag-Llama [57] MOMENT 24) with three thresholding methods:
Percentile B 42 MAD 29 EVT-POT 63 67
convert anomaly scores binary labels.
4.1 Comparison with various prompting strategies in LLM-based TSAD
4.1.1 Main results Table D shows that our proposed method substantially improves to TSAD performance across wide range of I.LMM Qwen-2.5, Gemini-1.5, 15, and GPT-4o. In every case, both the standard and affiliation-bases precision, recall, and Fl scores rise well above those of existing prompt baselines. Earlier methods often struggled to surpass naive basclines and occasionally underperformed them. In contrast, our method delivers clear, consistent gains that are not merely relative improvements among weak baselines, but absolute advances that surpass the naive baseline by a significant margin.
When comparing our Oshot-fext prompt with that of AnomLLM, we observe a substantial performance gain, even under identical input conditions. While combining vision and text can offer advantages, our results demonstrate that text alone, when combined with techniques such as de-seasonalization and index-aware prompting, can effectively support TSAD and challenge the notion that time series text is inherently limited for TSAD.

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Table 1: TSAD results on the A benchmark. We report standard and affiliation metrics. "Avg. Diff." denotes the average difference from the baseline, helping to reveal meaningful gains beyond dataset bias. Cells are shaded green/red to indicate values above/below the baseline. The comparison with the other 18 prompts is provided in Appendix
Standard Metries
Affiliation Metrics
LLMs
Prompt Methods
Prec.
Recall
F1
Avg. Dift.
Prec.
Recall
F1
Avg. Dift.
Naive haseline (Always Normal Predictor)
31.75
31,75
31.75
31.75
31.75
31.75
A (Oshot (Oshot-Text)
12.94
22.15
13.38
-15.59
22.12
28.38
24.10
-6.88
InterVLL2
A (Oshot-Vision)
22.33
46.19
23,92
-0.94
50.93
60.94
55.53
24.05
(LLaMA3-76B)
L (Ishot-Vision Vision CoT)
33.72
36.28
33.67
2.81
53.62
55.55
53,93
22.62
Our (Oshot-Text)
27.67
$2.29
29.21
4.64
54.31
60.71
55.04
24.94
(Oshot Text- Vision)
30.00
59.35
34.66
9.59
58.14
62.90
32.53
AnomLLM Texto
25.99
25.66
25.49
-6.04
45.69
42.44
43.11
12.00
A (Oshat-Vision)
45.78
51.00
46.65
16.06
69.50
68.99
68.78
37.34
Qwen-2.5 (VL-72B- Inst.)
AnomLLM (Ishot-Vision CoT)
27.09
29.08
27.23
-3.95
39.52
40.07
39.38
7.91
(Ushot: Text)
72.47
54,09
29.15
82.91
78.40
78.96
48.
Our (Oshot-Tess) Text-Vision)
72.95
69.78
67.56
37.31
84.51
81.52
81.87
50,88
Anoml. L.M.L.MM (Oshot-Text)
2.01
2.47
1.89
29.66
24.90
25.558
24.14
-6.88
Gemini- 1.5
AnomLLM (Oshot- (Oshot-Vision)
39.60
59.62
43.56
15.84
62.30
63.00
62.32
30.79
(Flash)
AnomLLM (Ishot-Vision-CoT)
35.61
37.85
35.20
4.47
66.06
61.92
62.45
31.73
Our (Oshot-Test)
57.81
43.78
46.62
17.65
66.16
64.33
64.12
33.12
Our (0shot-Tess Text-Vision)
68.29
65.81
63.31
34,05
84.12
81.94
82.05
50.95
AnomL.LM (Oshot-Text)
19.69
17.62
17.76
13.39
46.35
45.51
44.73
13.78
AnomLLM (Oshot-Vision)
39.54
50.60
42.03
12.31
62.19
61.99
61.68
30.20
GPT-4o
AttoLLLM (Ishot-Vision Vision-CoT)
31.48
38.50
32.93
2.55
56.43
55.84
55.39
24.14
Our (Oshot-Teex)
65.52
53.45
54.40
26.04
75.65
73.88
73.47
42.5%
(0shot Text Vistin)soon)
76.94
68.42
70.04
40.05
83.10
78.85
80.11
48.94
Table 2: Comparison of TSAD performance gains across L.MMs and their general reasoning ability benchmarks. The TSAD improvements positively correlate with general reasoning ability, as models with higher MMLUU-ro/MMMU scores (e.g.. GPT-4o, Qwen2.5) yield substantial F1 gains, whereas MMeerVL2-LLMMAR
exhibits only marginal improvement.
LLMs
GPT-4o
Qwen2.5-VI.772--nst.
Gemini-L.5-Flash
IntemnV.2-LLaMM33-66
TSAD (F1)- - AnomLLM
42.03
46.65
43.56
33.67
TSAD (F1) Ours
70.04
67.56
63.31
34.66
A TSAD (Ours- AnomLLM)
+28.01
+20.91
+19.75
+0.99
MMMU (Visual Reasoning)
69.10
64.50
56. 10
58.
MMLU (Language Reasoning)
74.68
71.59
64.09
56.20
4.1.2 Model-wise performance discrepancy and analysis of underlying factors While our proposed prompting strategy improved performance across all evaluated LLMs, the magnitude of im- provement varied notably among models. GPT-4o, Swwen..-VV-72B--- Instruct, and Gemini-1.5-FFlas showed significant gains, whereas Intern 1..--LL222...... 76B achieved only marginal improvement. To explore this discrepancy, we examined whether general reasoning ability correlates with improvement in TSAD. We compared models' scores on MMLU-Pro (language reasoning) and MMMU | (visual reasoning) benchmarks with their TSAD results. As shown in Table 2 the models with stronger reasoning capabilities, such as GPT-4o (MMLU 74.68, MMMU 69.10 and Qwen (71.59, 64.50), exhibited larger F1 improvements (+28.01 01 and +20.91), while LLaMA3 (MMLU 56.20, MMMU 58.20) improved by only +0.99. This trend suggests that high general reasoning ability contributes to more effective anomaly detection. Given the computational cost of full TSAD evaluations, aggregate reasoning benchmarks may offer a practical, lightweight proxy for estimating TSAD capability.
4.2 In-depth analysis
4.2.1 Ablation study Table 3 shows that each component of the proposed method plays mean- ingful and complementary role in improving L.-M-based TSAD. Removing the de-sensonalization step consistently lcads to drop of 5.64 Fl points on average across the three LLMs, confirming that
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Table 3: Results of the ablation study on our method, evaluated with standard metries.
LLMS
GPT-4o
Sween2.5-VI--22--.
Gemini-I.5-Flash
Avg.
Metric
Prec.
Recall
F1
Prec.
Recall
F1
Prec.
Recall
F1
Drop
Ours (Full)
76.94
68.42
70.04
72.95
69.78
67.56
68.29
65,81
63.31
w/o De-scasonalization
70.76
64.34
65.78
63.99
62.89
61.46
62.86
60.86
59.44
-5.64
w/o Index of time-series
41.03
43.99
41.13
38.67
43.49
38.73
49.03
67.69
52.90
-23.36
w/o Value of time-series text
41.06
46,25
44.47
72.47
54.09
56.13
45.66
61.94
49.11
-16.88
w/o Time-series image
65.52
53.45
54.40
65.52
53.45
54.40
57.82
43.78
46.62
-14.24
eliminating dominant seasonal patterns enables the model to better capture subtle trend and frequency shifts. particular, the exclusion of index information from the time-series text results in the most severe performance degradation, indicating that LLMs struggle with implicit positional reasoning and significantly benefit from explicit indexing for accurate localization. Omitting either the value in- formation or the sequence image leads to a performance drop, indicating that each modality provides complementary cues. Their combination consistently enhances detection accuracy, suggesting that LLMs effectively integrate textual and visual signals for more precise anomaly detection.
4.2.2 Can LLMs perform time-series decomposition without external tool? The above question holds merit in evaluating the time-series understanding capabilities of LLMs and guiding the design of corresponding detection frameworks that decide whether or not to invoke external tool or not. We assess the decomposition ability LLMs through two tasks: (1) detecting the presence of components and (2) generating each component's s sequence individually.
(a) Component Detection
(b) Compunent Genertiion
(c) Generation results components: Trend (lop) Seacuality (boliom)
3.0
P15cmm
LLM undel
400
600
1000
0.35
0.5
0.00
6-
Trond
Stasonality
6.
Trend
Secsonality
Comparison of LLMs and statistical decomposition for time-series component analysis. (a) shows component detection results, highlighting how well LLMs identify underlying component compared to statistical decomposition. (b) presents quantitative and (c) qualitative results evaluating the accuracy and fidelity of components generated by the LLM. The prompts used in this experiment are illustrated in Figure| 17
Figure 6:
Figure6]ssows shows that LLMs perform comparably to statistical decomposition in identifying the presence of individual components. However, their performance deteriorates significantly when generating sequences for each component individually, especially for long time series, where qualitative examples reveal notable errors and failure cases. These findings indicate that they require external tools to accurately disentangle and reconstruct those components. Nevertheless, as they succeed in identifying the presence of a component, we argue that LLMs are capable of deciding when invoke an external decomposition tool, in line with the emerging paradigm of tool-using LLMss. Further implementation details and examples of the decomposition experiment, including prompt templates and evaluation settings, are provided in Appendix D.4.
4.2.3 Index-Free vs. Index-Aware Existing studies 43 89 have argued that longer input tokens may degrade LLM performance, and 226 48 also prefer index-free prompts to keep inputs concise by omitting position indices. This motivates direct comparison between index-fre and index-aware prompts in the GT GT-provided setting. As shown in Table LLMs perform worse with index free prompts, recording significantly lower F1 scores. Index-aware prompts, despite roughly doubling the input token length, simplify the task by turning fragile position counting into direct retrieval, resulting in consistently higher localization accuracy. The input length influences the inference time, but the wall-clock latency primarily determined by the number of output tokens. In the specific case of Qwen, index-free free prompts occasionally resulted in verbose rambling outputs (Appendix
8

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Table Performance comparison of TSAD with GT labels between index-free prompts (using only values) and index-aware prompts (using both indices and values).
Backhone
Prompt
GT
Avg. Input Tokens
Avg. Output
Tokens
Avg, Inference Time (6)
Standard Metric
Afiliiation Metric
Recall
F1
Prec.
Recall
Gemini-15 (Flash)
Baseline
X
4968
53.06
0.84
2.01
2.47
1.89
24.90
25.58
24.14
Index free
/
5167
50.72
0.87
27.66
28.12
27.74
52.03
47.77
48.97
Index-aware
10258
50.83
1.20
64.79
62.46
62.83
67.88
67.54
67.39
Qwen-2.5 (VL.722BIIms,
Baseline
x
4965
132.91
10.46
25.99
25.66
25.49
45.69
42.44
43.11
Index-free
/
5161
573.05
29.79
20.31
20.39
20.33
31.06
29.37
29.84
Index-aware
10051
39.01
6.45
60.44
64.47
60.16
73.71
72.87
72.57
which in turn substantially increased decoding length and inference time. Although cannot pinpoint the exact cause, the lack of positional indices may have contributed by offering less concrete structure for the model to follow. Ultimately, including index tokens adds minor input overhead, but consistently leads more dependable and accurate TSAD.
Figure 19
Discussion and limitation
In this section, evaluate the practical value of using LLMs for TSAD. Although simple method improves detection performance, it remains important to assess whether LLMs offer tangible advantages over conventional models. Previous works highlight two main benefits: (1) zero-shot capability without domain-specific training and (2) the ability to incorporate contextual information expressed in natural language. However, these advantages have not been extensively evaluated in comparison to non-LLM methods. we examine the extent to which current L.-M-baeed methods deliver on these promises in real-world scenarios.
5.1 Comparison with conventional non-LLM TSAD methods
Fl Scores by Models
Inference Times by Models
4.2
Avg. E1) sooe
Avg. Inforcacc
100
0.7
Meard Lidgg--44
Arg ol Dityy-4i
0.1
Miatchaat
Pis
Aranaplja
Oveiltaraa
ITS
fimeth
Lay Lire
MOKOEN
Desige
CSS
UsD
hure
Al Warn Autt
Opt gt--k
Anomaly basad
Domainn- spoctfic
Domain specific DJ. boselines
Zero-shot
(Use the best of the thre thmsihllding methors)
MI. haselines
FM baselines
Figure 7: Comparison of F1 score and inference speed on the TSB-AD-U benchmark between 16 domain-specific ML/DL models and zero-shot FM/LLM-based models without any training.
We begin by comparing the accuracy and inference time of conventional AD methods and LLM-based methods using the TSB-AD-U benchmark. Unlike L.LM-based methods, conventional methods typically produce anomaly score for each timestamp, followed by thresholding step determine anomaly segments or binary label sequences. Since these methods are designed to compute scores that optimally separate anomalies from normal patterns, thresholding is treated Consequently, evaluations are typically based on score-based metrics or the best Fl score obtained using GT labels.
orthogonal task.
To enable a fair comparison with LLM-based methods that directly predict anomaly labels, we adopt label-based evaluation metrics across all models. For conventional methods, employ three widely used thresholding methods, such as percentile, MAD, and EVT-POT, and report the best-performing result among them. In terms of inference time, we measure the average inference time per sample for each method. To reduce the overall experimental cost, we limit the evaluation set by selecting time series from eight categories in the TSB-AD-L benchmark, focusing those with relatively shorter lengths. Detailed model descriptions. full benchmark results, and dataset statistics are provided in Appendix C.3 Appendix Table 9 and Appendix B.2 respectively.
Figure demonstrates that our -.aMe method achieves the highest average score on the benchmark, indicating strong accuracy compared to non-LLM methods. While the method performs

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reasonably well even in the absence of domain- domain-specifie training, its inference speed is significantly slower because of the overhead of decoding anomaly segments into textual descriptions. This trade- off underscores the promise of the method in low-resource or offline settings, while simultaneously revealing a key limitation in real-time scenarios.
5.2 Excluding known anomalies using context
(a) Real-world anomalies in Electricity
(b) Real-world anomalies in ETTh2
My Mwa
mmm ww www Pliterte contectan explorrable anomasy
Flbeinn coctostually oeplamalle seoreals
Prosersed Predictions
T Temporal Contect:
On the evening of Pebruary 7, temporary electricity usage restriction was implemented.
Temporal Context:
The sensing system is suspended the end of each month for maintenance.
Pattern Context: The usage is occastonally restricted below 1300 wi whot notice for sudden system maintenance.
Pattern Contest flat time sertes pattern may simply result from handling missing values caused by system shutdowrs.
Figure 8: Qualitative results on two real-world time series, including anomalies commonly detected by conventional TSAD models and our (GPT-4o). The LLM incorporates sequential and contextual information to exclude explainable patterns while retaining other anomalies. The full prompt and the response are provided in Figure 20
Another advantage of LLM-based methods over conventional models their ability use context through reasoning. We explore whether LLMs can perform context-aware anomaly filtering to remove uninteresting anomalies and to concentrate novel anomalies. Previous studies 45 8 have focused mainly on detecting predefined anomaly types by using the context, but this is closer to pattern recognition than true anomaly detection. Also, real-world scenarios demand the discovery of previously unseen anomalies-thonn Using context to enhance the detection of known anomalies may bias the system toward those specific patterns, potentially reducing its sensitivity to unknown or novel failures. Thus, we pivot the use of contextual information in TSAD toward excluding known or uninteresting anomalies.
that deviate from normal behavior without prior examples.
Figure g illustrates qualitative results on time series data commonly used in forecasting I 88], including anomalous patterns commonly detected by conventional models. We compare LLM predictions under different contexts. Without context, the LLM behaves similarly to conventional models, identifying abrupt dips flat patterns anomalies. However, when a temporal context and prior knowledge of expected time-series behavior are provided, the LLM can filter out explainable anomalies from its predictions. This demonstrates distinctive strength of LLMs: the ability to integrate natural language context directly into inference, enabling interpretable context-aware filtering without rule-based systems or low-level code inspection.
Further implementation details, including the full prompt template (Figure 20) and the complete experimental setup for the context-aided TSAD analysis, presented in Appendix D.6, We hope these findings inspire future research on LLM-based TSAD, particularly through the development of diverse datasets and evaluation benchmarks that reflect wide range of real-world scenarios.
6 Conclusion
In this paper, we revisit the use of LLMs for TSAD by identifying two key challenges: temporal understanding and anomaly localization. Unlike prior work that focused solely on performance, we analyze these failure modes and propose a simple method, statistical decomposition with index-aware prompting, that improves detection accuracy without requiring additional training. The experimental results on benchmark datasets verify that our method consistently outperforms existing prompts and baseline models. We also discuss both a drawback of using LLMs real-time settings: their latency compared to conventional TSAD methods, and unique strength: context-aware filtering that better aligns with the semantic definition of anomalies. We believe our findings offer practical guidance on how and when to use LLMs effectively for TSAD.
10

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Acknowledgements
This work was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (RS-2019-11100055. Artificial Intelligence Graduate School Support Program (KAIST); RS-2024-00396828, Development of AI based low power 5G-A O-DU/O-CU), and the National Research Foundation of Korea (NRF) grant
funded by the Korea government (MSIT) (No. RS 28-2005-005556211..
References
[1] Electricity dataset.
12014.
[2] C. C. Aggarwal and C. C. Aggarwal. An introduction to outlier analysis. Springer, 2017.
[3] S. Ahmad, A. Lavin, S. Purdy, and Z. Agha. Unsupervised real-time anomaly detection for streaming data. Nearocomputing, 262:134-147, 2017.
[4] S. Alnegheimish, L. Nguyen, L. Berti-Equille, and K. Veeramachaneni. Large language models can be zero-shot anomaly detectors for time series? arXiv preprint wnxi::2405.1455. 2024.
[5] O. D. Anderson. Time-series. 2nd edn., 1976.
[6] A. Ansari, L. Stella, C. Turkmen, X. Zhang, P. Mercado, H. Shen, O. Shchur, S. S. Ranga- puram, S. P. Arango, S. Kapoor, et al. Chronos: Learning the language of time series. arXiv preprint arXiv:2403.078155 2024.
[7] Audibert, P. Michiardi, F. Guyard, S. Marti, and M. A. Zuluaga. Usad: Unsupervised anomaly detection on multivariate time series. In Proc. the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.
[8] M. Bachlin, M. Plotnik, D. Roggen, I. Maidan, J. M. Hausdorff, N. Giladi, and G. Troster. Wearable assistant for parkinson's disease patients with the freezing of gait symptom. IEEE Transactions on Information Technology in Biomedicine, 14(2):436-44) 2009.
[9] S. Bai, Chen, Liu, J. Wang, W. Ge, S. Song, K. Dang, P. Wang, S. Wang. J. Tang, H. Zhong, Y. Zhu, M. Yang, Z. Li, J. Wan, P. Wang, W. Ding, Z. Fu, Y. Xu, J. Ye, X. Zhang, Xie, Cheng, Zhang, Yang, H. Xu, and Lin. Qwen2.5-v technical report. arXiv preprint 2tt.22502..3933.. 2025.
[10] Bai, K. Chen, Liu, J. Wang, W. Ge, S. Song, K. Dang, P. Wang, S. Wang. J. Tang, H. Zhong, Y. Zhu, M. Yang, Z. Li, J. Wan, P. Wang, W. Ding, Z. Fu, Y. Xu, J. Ye, X. Zhang, Xie, Cheng, H. Zhang, Z. Yang, H. Xu, and J. Lin. Qwen2.5-vl technical report, 2025. URL https:///aaat: org/ abs//bbb00 13923
[11] Boniol, Paparrizos, Palpanas, and M. J. Franklin. Sand: streaming subsequence anomaly detection. Proceedings of the VLDB Endowment, 14(10):117-1122,, 2021.
[12] G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung. Time series analysis: forecasting and control. John Wiley & Sons, 2015.
[13] L. Brunner and A. Voigt. Pitfalls in diagnosing temperature extremes. Nature Communications, 15(1):2087, 2024.
[14] Z. Chen, W. Wang, H. Tian, S. Ye, Z. Gao, E. Cui, W. Tong, K. Hu, J. Luo, Z. Ma, et al. How far we to gpt-4v? closing the gap commercial multimodal models with open open-source suites. arXiv preprint 2rXhv:2404.16822 2024.
[15] Z. Chen, WU, W. Wang, Su, Chen, Xing, M. Zhong, Q. Zhang, Zhu, L. Lu, et al. Internvl: Scaling up vision foundation models and aligning for generic visual- lingus-ii tasks. In Proceedings of the JEFE/CVF Conference on Computer Vision and Pattern Recognition, pages 24185-24198, 2024.
11

---

[16] H. Cheng, Q. Wen, Y. Liu, and L. Sun. Robusttsf: Towards theory and design of robust time series forecasting with anomalies. In Proc. the International Conference on Learning Representations (ICI.R), 2024.
[17] R. B. Cleveland, W. S. Cleveland, J. E. McRae, and I. Terpenning. Stl: A seasonal-trend decomposition. J. Off. Stat, 6(1):3-73, 1990.
[18] A. Das, W. Kong, R. Sen, and Y. Zhou. A decoder-only foundation model for time-series forecasting, In Proc. the International Conference on Machine Learning (ICML), 2024.
[19] N. Deng, Z. Sun, R. He, A. Sikka, Y. Chen, L. Ma, Y. Zhang, and R. Mihalcea. Tables as texts or images: Evaluating the table reasoning ability of Ilms and milms. In Findings of the Association for Computational Linguistics 2024, pages 407-426, 2024.
[20] M. Dong, H. Huang, and L. Cao. Can Ilms serve as time series anomaly detectors? arXiv preprint arXiv:2408. 03470, 2024.
[21] P. Fleith. Controlled anomalies time series (cats) dataset, September 2023, Accessed: 2023-09-
01.
[22] T. Fu, R. Ferrando, J. Conde, C. Arriaga, and P. Reviriego. Why do large language models (Ilm) struggle to count letters?, 2024. httppp:://aaaii.or//aaaatt aww12 18626
[23] A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and E. Stanley. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. circulation, 101(23); e215-e220, 2000.
[24] M. Goswami, K. Szafer, A. Choudhry, Cai, S. Li, and A. Dubrawski. Moment: A family of open time-series foundation models. In Proc. the International Conference on Machine Learning (ICML), 2024.
[25] S. D. Greenwald, R. S. Patil, and R. G. Mark. Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information. IEEE, 1990.
[26] N. Gruver, M. Finzi, S. Qiu, and A. G. Wilson. Large language models are zero-shot time series forecasters. Proc. the Advances in Neural Information Processing Systems (NeurIPS), volume 36, 2023.
[27] D. Gwak, J. Park, M. Park, C. Park, H. Lee, E. Choi, and J. Choo. Forecasting future international events: A reliable dataset for text-based event modeling. In Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.
[28] D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt. Measuring mathematical problem solving with the math dataset. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021.
[29] J. Hochenbaum, O. S. Vallis, and A. Kejariwal. Automatic anomaly detection in the cloud via statistical learning. arXiv preprint 1704.v: 2017.
[30] Y. Huang, J. Shi, Y. Li, C. Fan, S. Wu, Q. Zhang, Y. Liu, P. Zhou, Y. Wan, N. Z. Gong, al. Metatool benchmark for large language models: Deciding whether to use tools and which to use. arXiv preprint arx1v:2310.03128, 2023.
[31] A. Huet, J. M. Navarro, and D. Rossi. Local evaluation of time series anomaly detection algorithms. In Proc. the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pages 635-645, 2022.
[32] K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom. Detecting spacecraft anomalies using Istms and nonparametric dynamic thresholding. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pages 387-395, 2018.
[33] TOPS. lops anomaly detection dataset. Accessed: 2025-05-11.
http://iops.a/ftaaeet..eee
n.d.
12

---

[34] V. Jacob, F. Song. A. Stiegler, B. Rad, Y. Diao, and N. Tatbul. Exathlon: A benchmark for explainable anomaly detection over time series. arXiv preprint arXiv:2010.05073. 2020.
[35] M. Jin, Y. Zhang, W. Chen, K. Zhang, Y. Liang, B. Yang. J. Wang. S. Pan, and Q. Wen. Position paper: What can large language models tell us about time series analysis. In ICML, 2024.
[36] E. Keogh, J. Lin, S.-H. Lee, and H. V. Herle. Finding the most unusual time series subsequence: algorithms and applications. Knowledge and Information Systems, 11:1-27, 2007.
[37] G. Kirchgässner. J. Wolters, and U. Hassler. Introduction to modern time series analysis. Springer Science & Business Media, 2012.
[38] K.-H. Lai, D. Zha, Xu, Y. Zhao, G. Wang. and X. Hu, Revisiting time series outlier detection: Definitions and benchmarks. In Thirty-fifth conference on neural information processing systems datasets and benchmarks track (round 1), 2021.
[39] N. Laptev, S. Amizadeh, and Y. Billawala. S5 - a labeled anomaly detection dataset, version 1.0, Mar. 2015. 16M.
[40] S. Le Clainche and J. M. Vega. Higher order dynamic mode decomposition. SIAM Journal on Applied Dynamical Systems, 16(2):882-925, 2017. doi: 10.1137/15M0554924
[41] A. Lewkowyez, A. Andreassen, D. Dohan, E. Dyer, H. Michalewski, V. Ramasesh, A. Slone, C. Anil, I. Schlag. T. Gutman-Solo, al. Solving quantitative reasoning problems with language models. Advances in Neural Information Processing Systems, 35:3843-3883 2022.
[42] H. Li, X. Wang. S. Choy, C. Jiang, S. Wu, J. Zhang, C. Qiu, K. Zhou, L. Li, E. Fu, al. Detecting heavy rainfall using anomaly- percentile thresholds of predictors derived from gnss-pwv. Atmospheric Research, 265: 105912, 2022.
[43] T. Li, G. Zhang, Q. D. Do, X. Yue, and W. Chen. Long-context llms struggle with long in-context learning, arXiv preprint (.Xi:22404402066) 2024.
[44] F. T. Liu, K. M. Ting, and Z.-H. Zhou. Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining, pages 413-422, 2008 doi: 10. 10110000.2...008.17
[45] J. Liu, C. Zhang, Qian, M. Ma. S. Qin, C. Bansal, Q. Lin, S. Rajmohan, and D. Zhang. Large language models can deliver accurate and interpretable time series anomaly detection. arXiv preprint arXiv:2405. 15370, 2024.
[46] Q. Liu and J. Paparrizos. The elephant in the room: Towards a reliable time-series anomaly de- tection benchmark. In Proc. the Advances in Neural Information Processing Systems (NeurIPS), 2024.
[47] A. P. Mathur and N. O. Tippenhauer. Swat: A water treatment testbed for research and training on ics security. In 2016 international workshop on cyber-physical systems for smart water networks (CySWater), pages 31-36. IEEE, 2016.
[48] M. A. Merrill, M. Tan, V. Gupta, T. Hartvigsen, and T. Althoff. Language models still struggle to zero-shot reason about time series. In Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.
[49] M. Munir, S. A. Siddiqui, A. Dengel, and S. Ahmed. Deepant: A deep learning approach for unsupervised anomaly detection in time series. leee Access, 7: 7:1991-2005, 2018.
[50] NOAA Pacific Marine Environmental Laboratory. Tropical atmosphere ocean (tao) project.
https://www. http:/wwwwwpppp.....m./ noaa. gov/ Accessed: 2025-05-11.
[51]
OpenAI, :, A. Hurst, Lerer, A. P. Goucher, A. Perelman, A. Ramesh, A. Clark, A. Ostrow, A. Welihinda, A. Hayes, A. Radford, A. Mądry, A. Baker- Whitcomb, A. Beutel, A. Borzunov, A. Carney, A. Chow, A. Kirillov, A. Nichol, A. Paino, A. Renzin, A. T. Passos, A. Kirillov,
A. Christakis, A. Conneau, A. Kamali, A. Jabri, A. Moyer, A. Tam, A. Crookes, A. Tootoochian, A. Tootoonchian, A. Kumar, A. Vallone, A. Karpathy, A. Braunstein, A. Cann, A. Codispoti,
A. Galu, A. Kondrich, A. Tulloch, A. Mishchenko, A. Back, A. Jiang, A. Pelisse, A. Woodford,
13

---

A. Gosalia, A. Dhar, A. Pantuliano, A. Nayak, A. Oliver, B. Zoph. B. Ghorbani, B. Leimberger,
B. Rossen, B. Sokolowsky, B. Wang. B. Zweig, B. Hoover, B. Samic, B. McGrew, B. Spero. B. Giertler, B. Cheng. B. Lightcap, B. Walkin, B. Quinn, B. Guarraci, B. Hsu, B. Kellogg, B. Eastman, C. Lugaresi, C. Wainwright, C. Bassin, C. Hudson, C. Chu, C. Nelson, C. Li, C. Shern, C. Conger, C. Barette, C. Voss, C. Ding, C. Lu, C. Zhang, C. Beaumont, C. Hal- lacy, C. Koch, C. Gibson, C. Kim, C. Choi, C. McLeavey, C. Hesse, C. Fischer, C. Winter, C. Czarnecki, C. Jarvis, C. Wei, C. Koumouzelis, D. Sherburn, D. Kappler, D. Levin, D. Levy, D. Carr, D. Farhi, D. Mely, D. Robinson, D. Sasaki, D. Jin, D. Valladares, D. Tsipras, D. Li, D. P. Nguyen, D. Findla,, E. Oiwoh, E. Wong, E. Asdar, E. Prochl, E. Yang, E. Antonow, E. Kramer, E. Peterson, E. Sigler, E. Wallace, E. Brevdo, E. Mays, F. Khorasani, F. P. Such, F. Raso, F. Zhang, F. von Lohmann, F. Sulit, G. Goh, G. Oden, G. Salmon, G. Starace, G. Brockman, H. Salman, H. Bao, H. Hu, H. Wong, H. Wang, H. Schmidt, H. Whitney, H. Jun, H. Kirchner, H. P. de Oliveira Pinto, H. Ren, H. Chang, H. W. Chung, I. Kivlichan, I. O'Conelll I. O' O'Connell,
I. Osband, I. Silber, I. Sohl, I. Okuyucu, I. Lan, I. Kostrikov, I. Sutskever, I. Kanitscheider,
I. Gulrajani, J. Coxon, J. Menick, J. Pachocki, J. Aung. J. Betker, J. Crooks, J. Lennon, J. Kiros, J. Leike, J. Park, J. Kwon, J. Phang, J. Teplitz, J. Wei, J. Wolfe, J. Chen, J. Harris, J. Varavva, J. G. Lee, J. Shieh, J. Lin, J. Yu, J. Weng, Tang, J. Yu, Jang. J. Q. Candela, J. Beutler, J. Landers, J. Parish, J. Heidecke, J. Schulman. J. Lachman, J. McKay, J. Uesato, J. Ward, J. W. Kim, J. Huizinga, Sitkin, J. Kraaijeveld. J. Gross, Kaplan, J. Snyder, Achiam, J. Jiao, J. Lee, J. Zhuang, J. Harriman, K. Fricke, K. Hayashi. K. Singhal, K. Shi, K. Karthik, K. Wood, K. Rimbach, K. Hsu, K. Nguyen, K. Gu- Gu-Lemberg, K. Button, K. Liu, Howe, K. Muthukumar,
K. Luther, I.. Ahmad, L. Kai, L. Itow, I.. Workman, I.. Pathak, L. Chen, I.. Jing. I.. Guy,
L.. Fedus, L. Zhou, I.. Mamitsuka, I.. Weng. I.. McCallum, L.. Held, L. Ouyang, L.. Feuvrier, L. Zhang. L. Kondraciuk, L. Kaiser, L. Hewitt, L. Metz, L. Doshi, M. Aflak, M. Simens, M. Boyd, M. Thompson, M. Dukhan, M. Chen, M. Gray, M. Hudnall, M. Zhang, M. Aljubeh, M. Litwin, M. Zeng, M. Johnson, M. Shetty, M. Gupta, M. Shah, M. Yatbaz, M. J. Yang,
M. Zhong, M. Glaese, M. Chen, M. Janner, M. Lampe, M. Petrov, M. Wu, M. Wang. M. Fradin, M. Pokrass, M. Castro, M. O. T. de Castro, M. Pavlov, M. Brundage, M. Wang. M. Khan, M. Murati, M. Bavarian, M. Lin, M. Yesildal, N. Soto, N. Gimelshein, N. Cone, N. Staudacher, N. Summers, N. LaFontaine, N. Chowdhury, N. Ryder, N. Stathas, N. Turley, N. Tezak, N. Felix, N. Kudige, N. Keskar, N. Deutsch, N. Bundick, N. Puckett, O. Nachum, O. Okelola, O. Boiko, O. Murk, O. Jaffe, O. Watkins, O. Godement, O. Camphell-Moore, P. Chao, P. McMillan, P. Belov, P. Su, P. Bak, P. Bakkum, P. Deng, P. Dolan, P. Hoeschele, P. Welinder, P. Tillet, P. Pronin, P. Tillet, P. Dhariwal, Q. Yuan, R. Dias, R. Lim, R. Arora, R. Troll, R. Lin, R. G. Lopes, R. Puri, R. Miyara, R. Leike, R. Gaubert, R. Zamani, R. Wang, R. Donnelly, R. Honsby, R. Smith, R. Sahai. R. Ramchandani, R. Huet, R. Carmichael, R. Zellers, R. Chen, R. Chen, R. Nigmatullin, R. Cheu, S. Jain, S. Altman, S. Schoenholz, S. Toizer, S. Miserendino, S. Agar- wal, S. Culver, S. Ethersmith, S. Gray, S. Grove, S. Metzger, S. Hermani, S. Jain, S. Zhao, S. Wu, S. Jomoto, S. Wu, Shuaiqi, Xia, S. Phene, S. Papay, S. Narayanan, S. Coffey, S. Lee, S. Hall. S. Balaji, T. Broda, T. Stramer, T. Xu. T. Gogineni, T. Christianson, T. Sanders, T. Patwardhan, T. Cunninghman, T. Degry, T. Dimson, T. Raoux, T. Shadwell, T. Zheng, Underwood, T. Markov, T. Sherbakov, T. Rubin, T. Stasi, T. Kaftan, T. Heywood, T. Peter- son, T. Walters, T. Eloundou, V. Qi, V. Moeller, V. Monaco, V. Kuo, V. Fomenko, W. Chang, W. Zheng, W. Zhou, W. Manassra, W. Sheu, W. Zaremba, Y. Patil, Y. Qian, Kim, Y. Cheng, Y. Zhang. Y. He, Y. Zhang, Y. Jin, Y. Dai, and Y. Malkov. Gpt-4o system card, 2024. URL httpp::/arrat arrii.xxivvvv.. wwwpxiii.org//aao
21276
[52] J. Park, J. Lee, Y. Cho, W. Shin, D. Kim, J. Choo, and E. Choi. Deep imbalanced time- series forecasting via local discrepancy density. In Proc. of Machine Learning and Knowledge Discovery in Databases: Research Track (ECML/KKD)) pages 139-155. Springer Nature Switzerland, 2023. doi: 10. 10.10077978-3-031-444444 9. URL https://linn: springer com wwtptooeet/t///o...oo.. http://0.110//788-3-0---43322223
[53] J. Park, D. Gwak, J. Choo, and E. Choi. Self-supervised contrastive learning for long-term forecasting, In Proc. the International Conference Learning Representations (ICLR), 2024.
[54] J. Park, H. Lee, D. Lee, D. Gwak, and J. Choo. Revisiting 1lms as zero-shot time-series forecasters: Small noise can break large models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACI.), 2025. (R // //arxiv. org/ abs/ 2506. 000477
14

---

[55] P. Pasupat and P. Liang. Compositional semantic parsing ceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume I: Long Papers), pages 1470-1480, 1480, 2015.
tables. In Pro-
[56] C. Qian, C. Han, Y. Fung, Y. Qin, Z. Liu, and H. Ji. Creator: Tool creation for disentangling abstract and concrete reasoning of large language models. arXiv preprint arXiv:2305. 14318, 2023.
[57] K. Rasul, A. Ashok, A. R. Williams, A. Khorasani, G. Adamopoulos, R. Bhagwatkar, M. Biloš, H. Ghonia, N. Hassen, A. Schneider, et al. Lag-llama: Towards foundation models for time series forecasting. In RO-FoMo: Robustness of Few-shot and Zero-shot Learning in Large Foundation Models, 2023.
[58] C. RB. Stl: A seasonal-tren decomposition procedure based on loess. J Off Stat, 6:3-73, 1990.
[59] H. Ren, B. Xu, Y. Wang. C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang Time-series anomaly detection service microsoft. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining, KDD '19, page 3009-3017. ACM, July 2019. doi: 10. 10.11451399500:3336000 URLhttpp:
doi org/
10. 02925000 3330680
[60] H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang. J. Tong, and Q. Zhang. Teme-series anomaly detection service microsoft. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 3009-3017, 2019.
[61] D. Roggen, A. Calatroni, M. Rossi, T. Holleczek, K. Förster, G. Tröster, P. I Lukowicz, D. Ban- nach, G. Pirkl, A. Ferscha, al. Collecting complex activity datasets in highly rich networked sensor environments. In 2010 Seventh international conference on networked sensing systems (INSS), pages 233-240. IEEE, 2010.
[62] M. Sakurada and T. Yairi. Anomaly detection using autoencoders with nonlinear dimensionality reduction. In Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis, pages 4-11, 2014.
[63] C. Scarrott and A. MacDonald. A review of extreme value threshold estimation and uncertainty quantification. REVSSTAT-Staistical journal, 10(1):33-60 2012.
[64] H. Si, J. Li, C. Pei, H. Cui, J. Yang, Y. Sun, S. Zhang, J. Li, H. Zhang, J. Han, et al. Timeseries- bench: An industrial-grade benchmark for time series anomaly detection models. In 2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE), pages 61-72. IEEE, 2024.
[65] Y. Su, Y. Zhao, C. Niu, R. Liu, W. Sun, and D. Pei. Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 2828-2837, 2019.
[66] Y. Sui, M. Zhou, M. Zhou, S. Han, and D. Zhang. Table meets IIm: Can large language models understand structured table data? benchmark and empirical study. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pages 645-654, 2024.
[67] A. Tancredi, C. Anderson, and A. O'Hagan. Accounting for threshold uncertainty in extreme value estimation. Extremes, 9(2):87-106, 2006.
[68] H. Tang, C. Zhang, M. Jin, Q Yu, Z. Wang, X. Jin, Y. Zhang, and M. Du. Time series forecasting with llms: Understanding and enhancing model capabilities. ACM SIGKDD Explorations Newsletter, 2025.
[69] S. J. Taylor and B. Letham. Forecasting at scale. The American Statistician, 72(1):37-45 2018.
15

---

[70] G. Team, P. Georgiev. V. I. Lei, R. Burnell, L. Bai, A. Gulati, G. Tanzer, D. Vincent, Z. Pan, S. Wang. S. Mariooryad, Y. Ding. X. Geng, F. Alcober, R. Frostig. M. Omernick, L. Walker, C. Paduraru, C. Sorokin, A. Tacchetti, C. Gaffnny, S. Daruki, O Sercinoglu, 7. Gleicher, Love, Voigtlaender, R. Jain, G. Surita, K. Mohamed, R. Blevins, J. Ahn, T. Zhu, K. Kawintiranon, O. Firat, Y. Gu, Y. Zhang, M. Rahtz, M. Faruqui, N. Clay, J. Gilmer, J. Co-Reyes, I. Penchev, R. Zhu, N. Morioka, K. Hui, K. Haridasan, V. Campos, M. Mahdich, M. Guo, S. Hassan, K. Kilgour, A. Vezer, H.-T. Cheng. R. de Liedekerke, S. Goyal, P. Barham, D. Strouse, S. Noury, J. Adler, M. Sundararajan, S. Vikram, D. Lepikhin, M. Paganini, X. Garcia, F. Yang, D. Valter, M. Trebacz, K. Vodrahalli, C. Asawaroengchai, R. Ring, N. Kalb, L. B. Soares. S. Brahma, D. Steiner. T. Yu, F. Mentzer, A. He. L. Gonzalez, B. Xu, R. L. Kaufman, L. E. Shafey, J. Oh,
T. Hennigan. G. van den Driessche, S. Odoom, M. Lucic, B. Roelofs, S. Lall, A. Marathe, B. Chan, S. Ontanon, I.. He, D. Teplyashin, J. Lai, P. Crone, B. Damoc, I.. Ho, S. Riedel, K. Lenc, C.-K. Yeh, A. Chowdhery, Y. Xu, M. Kazemi, E. Amid, A. Petrushkina, K. Swersky, A. Khodaei, G. Chen, C. Larkin, M. Pinto, G. Yan, A. P. Badia, P. Patil, S. Hansen, D. Orr, S. M. R. Arood, J. Grimstad, A. Dai, S. Douglas, R. Sinha, V. Yadav, X. Chen, E. Gribovskaya, J. Austin, J. Zhao, K. Patel, P. Komarek, S. Austin, S. Borgeaud, L. Friso, A. Goyal, B. Caine, K. Cao, D.-W. Chung, M. Lamm, G. Barth-Maron, T. Kagohara, K. Olszewska, M. Chen, K. Shivakumar, R. Agarwal, H. Godhia, R. Rajwar, J. Snaider, X. Dotiwalla, Y. Liu, A. Barua, V. Ungureanu, Y. Zhang, B.-O. Batsaikhan, M. Wirth, J. Qin, Danihelka, T. Doshi, M. Chad- wick, J. Chen, S. Jain, Q. Le, A. Kar, M. Gurumurthy, C. Li, R. Sang. F. Liu, L. Lamprou, R. Munoz, N. Lintz, H. Mehta, H. Howard, M. Reynolds, L. Aroyo, Q. Wang, L. Blanco, A. Cassirer, J. Griffith, D). Das, S. Lee, J. Sygnowski, Z. Fisher, J. Besley, R. Powell, 7. Ahmed, D. Paulus, D. Reitter, Z. Borsos, R. Joshi, A. Pope. S. Hand, V. Selo, V. Jain, N. Sethi, M. Goel, T. Makino, R. May. Z. Yang. J. Schalkwyk, C. Butterfield, A. Hauth, A. Goldin, W. Hawkins, E. Senter, S. Brin, O. Woodman, M. Ritter, E. Noland, M. Giang, V. Bolina, L. Lee, Blyth. I. Mackinnon, M. Reid, O. Sarvana, D. Silver, A. Chen, L. Wang, L. Maggiore, O. Chang, N. Attaluri, G. Thornton, C.-C. Chiu, O. Bunyan, N. Levine, T. Chung, E. Eltyshev, X. Si, T. Lillicrap. D. Brady, V. Aggarwal, B. Wu, Y. Xu, R. Mcllroy, K. Badola, P. Sandhu, E. Moreira, W. Stokowiec, R. Hemsley, D. Li, A. Tudor, P. Shyam, E. Rahimtoroghi, S. Haykal, P. Sprech- mann, X. Zhou, D. Mineu, Y. Li, R. Addanki, K. Krishna. X. Wu, A. Frechette, M. Eyal, A. Dafoe, D. Lacey, J. Whang, T. Avrahami,, Y. Zhang, E. Taropa, H. Lin, D. Toyama, E. Ruther- ford, Sano, H. Choe, A. Tomala, C. Safranek-Shrader, N. Kassner, M. Pajarskas, M. Harvey, S. Sechrist, M. Fortunato, C. Lyu, G. Elsayed, C. Kuang, J. Lottes, E. Chu, C. Jia, C.WW. Chen, P. Humphreys, K. Baumli, C. Tao, R. Samuel, C. N. dos Santos, A. Andreassen, N. Rakićević, D. Grewe, A. Kumar, S. Winkler, J. Caton, A. Brock, S. Dalmia, H. Sheahan, I. Barr, Y. Miao, Natsev, J. Devlin, F. Behbahani, F. Prost, Y. Sun, A. Myaskovsky, T. S. Pillai, D. Hurt, A. Lazaridou, X. Xiong, C. Zheng, F. Pardo, X. Li, D. Horgan, J. Stanton, M. Ambar, F. Xia, A. Lince, M. Wang, B. Mustafa, A. Webson, H. Lee, R. Anil, M. Wicke, T. Dozat, A. Sinha, E. Piqueras, E. Dabir, S. Upadhyay, A. Boral, L. A. Hendricks, C. Fry, J. Djolonga, Y. Su, J. Walker, J. Labanowski, R. Huang. V. Misra, J. Chen, R. Skerry-Ryan, A. Singh, S. Rijhwani, D. Yu, A. Castro- Ros, B. Changpinyo, R. Datta, S. Bagri, A. M. Hrafnkelsson, M. Maggioni, D. Zheng, Y. Sulsky, S. Hou, T. L. Paine, A. Yang. J. Riesa, D. Rogozinska, D. Mareus, D. E. Badawy, Q. Zhang. L. Wang, H. Miller, Greer, L. L. Sjos, A. Nova, H. Zen, R. Chaabouni, M. Rosca, J. Jiang, C. Chen, R. Liu, T. Sainath, M. Krikun, A. Polozov, J. Lespiau, Newlan, Z. Cankara, S. Kwak, Y. Xu, P. Chen, A. Coenen, C. Meyer, K. Tsihlas A. Ma, J. Gottweis, J. Xing. C. Gu, J. Miao, C. Frank, Z. Cankara, S. Ganapathy, I. Dasgupta, S. Hughes-Fitt, H. Chen, D. Reid, K. Rong. H. Fan, J. van Amersfoort, V. Zhuang, A. Cohen, S. S. Gu, A. Mo- hananey, A. Ilie, T. Tobin, J. Wieting, A. Bortsova, P. Thacker, E. Wang, E. Caveness, J. Chiu, E. Sezener, A. Kaskasoli, S. Baker, K. Millican, M. Elhawaty, K. Aisopos. C. Lebsack, N. Byrd, H. Dai, W. Jia, M. Wiethoff, E. Davoodi, A. Weston, L. Yagati, A. Ahuja, I. Gao, G. Pundak, S. Zhang, M. Azzam, K. C. Sim, S. Caelles, J. Keeling, A. Sharma, A. Swing. Y. Li, C. Liu, C. G. Bostock, Y. Bansal, Z. Nado, A. Anand, J. Lipschultz, A. Karmarkar, L. Proleev, A. Itty- cheriah, S. H. Yeganeh, G. Polovets, A. Faust, J. Sun, A. Rrustemi, P. Li, R. Shivanna, J. Liu, C. Welty, F. Lebron, A. Baddepudi, S. Krause, E. Parisotto, R. Soricut, Z. Xu, D. Bloxwich, M. Johnson, B. Neyshabur, J. Mao-Jones, R. Wang, V. Ramasesh, Z. Abbas, A. Guez, C. Segal, D. D. Nguyen, J. Svensson, L. Hou, S. York, K. Milan, S. Bridgers, W. Gworek, M. Tagliasac- chi, J. Lee-Thorp. M. Chang, A. Guseynov, A. J. Hartman, M. Kwong, R. Zhao, S. Kashem, E. Cole, A. Miech, R. Tanburn, M. Phuong, F. Pavetic, S. Cevey, R. Comanescu, R. Ives, S. Yang, C. Du, B. Li, Z. Zhang, M. linuma, C. H. Hu, A. Roy, S. Bijwadia, Z. Zhu, D. Martins,
16

---

R. Suputro, A. Gergely, S. Zheng, D. Jia, I. Antonoglou, A. Sadovsky, S. Gu, Y. Bi, A. Andreev,
S. Samangooei. M. Khan, T. Kocisky, A. Filos, C. Kumar, C. Bishop, A. Yu, S. Hodkinson, S. Mittal, P. Shah, A. Moufarek, Y. Cheng, A. Bloniarz, Lee, P. Pejman, P. Michel, S. Spencer, V. Feinberg, X. Xiong, N. Savinov, C. Smith, S. Shakeri, D. Tran, M. Chesus, B. Bohnet,
G. Tucker, T. von Glehn, C. Muir, Y. Mao, H. Kazawa, A. Slone, K. Soparkar, D. Shrivastava, J. Cobon-Kerr, M. Sharman, J. Pavagadhi, C. Araya, K. Misiunas, N. Ghelani, M. Laskin,
D. Barker, Q. Li, A. Briukhov, N. Houlsby, M. Glaese, B. Lakshminarayanan, N. Schucher, Y. Tang, E. Collins, H. Lim, F. Feng, A. Recasens, G. Lai, A. Magni, N. D. Cao, A. Siddhant, Z. Ashwood, J. Orbay, M. Dehghani, J. Bremnan, Y. He, K. Xu, Y. Gao. C. Saroufim, J. Molloy, X. Wu, S. Amold, S. Chang, J. Schrittwieser, E. Buchatskaya, S. Radpour, M. Polacek, S. Gior- dano, A. Bapna, S. Tokumine, V. Hellendoom,, T. Sottiaux, S. Cogan, A. Severyn, M. Saleh, S. Thakoor, I.. Shefey, S. Qiao, M. Gaba, S. yiin Chang, C. Swanson, B. Zhang. B. Lee, P. K. Rubenstein, G. Song. T. Kwiatkowski, A. Koop, A. Kannan, D. Kao, P. Schuh, A. Stjerngren, G. Ghiasi G. Gibson, L. Vilnis, Y. Yuan, F. T. Ferreira, A. Kamath, T. Klimenko, K. Franko, K. Xiao, I. Bhattacharya, M. Patel, R. Wang. A. Morris, R. Strudel, V. Sharma, P. Choy, S. H. Hashemi, J. Landon, M. Finkelstein, P. Jhakra, J. Frye, M. Barnes, M. Mauger, D. Daun, K. Baatarsukh, M. Tung, W. Farhan, H. Michalewski, F. Viola, F. de Chaumont Quitry, C. L. Lan, T. Hudson, Q. Wang. F. Fischer, I. Zheng, E. White, A. Dragan, J. baptiste Alayrac, E. Ni, A. Pritzel, A. Iwanicki, M. Isard, A. Bulanova, L. Zilka,
Dyer, D. Sachan, S. Srinivasan, H. Muckenhirn. H. Cai, A. Mandhane, M. Tariq, J. W. Rae, G. Wang, K. Ayoub, N. FitzGer- ald, Y. Zhao, W. Han, C. Alberti,
Garrette, K. Krishnakumar, M. Gimenez, A. Levskaya, D. Sohn, J. Matak, I. Iturrate, M. B. Chang, J. Xiang, Y. Cao. N. Ranka, G. Brown, A. Hut- ter, V. Mirrokni, N. Chen, K. Yao, Z. Egyed, F. Galilee, T. Liechty, P. Kallakuri, S. Ghemawat, J. Liu, D. Tao, C. Thornton, T. Green, M. Jasarevic, S. Lin, V. Cotruta, Y.-X. Tan, N. Fiedel, H. Yu, E. Chi, A. Neitz, J. Heitkaemper, A. Sinha, D. Zhou, Y. Sun, C. Kaed, B. Hulse, S. Mishra, M. Georgaki, S. Kudugunta, C. Farabet, I. Shafran, D. Vlasic, A. Tsit- sulin, R. Ananthanarayanan, A. Carin, G. Su, P. Sun, S. V, G. Carvajal, J. Broder, I. Comsa, A. Repina, W. Wong, W. W. Chen, P. Hawkins, E. Filonov, L. Loher, C. Hirnschall, W. Wang, J. Ye, A. Burns, H. Cate, D. G. Wright, F. Piccinini. L. Zhang, C.-C. Lin, I. Gog, Y. Kulizh- skaya, A. Sreevatsa, S. Song, L. C. Cobo. A. Iyer, C. Tekur, G. Garrido, Z. Xiao, R. Kemp, H. S. Zheng, H. Li, A. Agarwal, C. Ngani, K. Goshvadi, R. Sittmmaria-Fernadderr W. Fica, X. Chen, C. Gorgolewski, S. Sun, R. Garg, X. Ye, S. M. A. Eslami, N. Hua, J. Simon, P. Joshi, Y. Kim, I. Tenenny S. Potluri, L. N. Thiet, Q. Yuan, F. Luisier, A. Chronopoulou, S. Scellato, P. Srinivasan, M. Chen, V. Koverkathu, V. Dalibard, Y. Xu, B. Saeta, K. Anderson, T. Sellam, N. Fernando, F. Huot, J. Jung. M. Varadarajan, M. Quinn, A. Raul, M. Le, R. Habalov, J. Clark, K. Jalan, K. Bullard, A. Singhal, T. Luong, B. Wang, S. Rajayogam, J. Eisenschlos, J. Jia, D. Finchelstein, A. Yakubovich, D. Balle, M. Fink, S. Agarwal, J. Li, D. Dvijotham, S. Pal, K. Kang, J. Konzelmann, J. Beattie, O. Dousse, D. Wu, R. Crocker, C. Elkind, S. R. Jonnala- gadda. J. Lee, D. Holtmann Biltm, K. Kallarackal, R. Liu, D. Vnukov, N. Vats, L. Invernizzi. M. Jafari, H. Zhou, L. Taylor, J. Prendki, M. Wu, T. Eccles, T. Liu, K. Kopparapu, F. Beaufays. C. Angermueller, A. Marzoca, S. Sarcar, H. Dib, J. Stanway, F. Perbet, N. Trdin, R. Sterneck, A. Khorlin, D. Li, X. Wu, S. Goenka, D. Madras, S. Goldshtein, W. Gierke, T. Zhou, Y. Liu, Y. Liang, A. White, Y. Li, S. Singh, S. Bahargam, M. Epstein, S. Basu, L. Lao, A. Ozturel, C. Crous, A. Zhai, H. Lu, Z. Tung, N. Gaur, A. Walton, L. Dixon, M. Zhang. A. Globerson, G. Uy. A. Bolt O. Wiles, M. Nasr, I. Shumailov, M. Selvi, F. Piccinno. R. Aguilar. S. McCarthy, M. Khalman, M. Shukla, V. Galic, J. Carpenter, K. Villela, H. Zhang. H. Richardson, J. Martens, M. Bosnjak, S. R. Belle, J. Seibert, M. Alnahlawi, B. McWilliams, S. Singh, A. Louis, W. Ding, D. Popovici, L. Simicich, L. Knight, P. Mehta, N. Gupta, C. Shi, S. Fatehi, Mitrovic, A. Grills, J. Pagadora, T. Munkhdalai, D. Petrova, D. Eisenbud, Z. Zhang, D. Yates, B. Mittal, N. Tripura- neni, Y. Assael, T. Brovelli, P. Jain, M. Velimirovic, C. Akbulut, J. Mu, W. Macherey, R. Kumar, J. Xu, H. Qureshi, G. Comanici, J. Wiesner, Z. Gong. A. Ruddock, M. Bauer, N. Felt, A. GP, A. Arnab, D. Zelle, J. Rothfuss, B. Rosgen, A. Shenoy, B. Seybold, X. Li, J. Mudigonda, G. Erdogan, J. Xia, J. Simsa, A. Michi, Y. Yao, C. Yew, S. Kan, I. Caswell, C. Radebaugh, A. Elisseeff, P. Valenzuela, K. McKinney, K. Paterson, A. Cui, E. Latorre-Chimotee S. Kim, W. Zeng, K. Durden, P. Ponnapalli, T. Sosea, C. A. Choquette-Choo, J. Manyika, B. Robenek, H. Vashisht, S. Pereira, H. Lam, M. Velic, D. Owusu-Afriyie, K. Lee, T. Bolukbasi, A. Parrish, S. Lu, J. Park, B. Venkatraman, A. Talbert, L. Rosique, Y. Cheng, A. Sozanschi, A. Paszke, P. Kumar, J. Austin, L. Li, K. Salama, B. Perz, W. Kim, N. Dukkipati, A. Baryshnikov, C. Ka- planis, X. Sheng, Y. Chervonyi, C. Unlu, D. de Las Casas, H. Askham, K. Tunyasuvunakool,
Palmer,
17

---

F. Gimeno, S. Poder, C. Kwak, M. Miecnikowski, V. Mirrokni, A. Dimitriev, A. Parisi, D. Liu,
T. Tsai, T. Shevlane, C. Kouridi, D. Garmon, A. Goedeckemeyer, A. R. Brown, A. Vijayakumar,
A. Elqursh, S. Jazayeri, J. Huang, S. M. Carthy, J. Hoover, I.. Kim, S. Kumar, W. Chen, C. Biles, G. Bingham, F. Rosen, L.. Wang. Q. Tan, D. Engel, F. Pongetti, D. de Cesare, D. Hwang. I.. Yu,
J. Pullman, S. Narayanan, K. Levin, S. Gopal, M. Li, A. Aharoni, T. Trinh, J. Lo, N. Casagrande,
R. Vij. L. Matthey, B. Ramadhana, A. Matthews, C. Carey, M. Johnson, K. Goranova, R. Shah, S. Ashraf, K. Dasgupta, R. Larsen, Y. Wang, M. R. Vuyyuru, C. Jiang. J. Ijazi, K. Osawa, C. Smith, R. Boppana, T. Bilal, Y. Koizumi, Y. Xu, Y. Altun, N. Shabat, B. Bariach, A. Ko- rehemniy, K. Choo. O. Ronneberger, C. Iwuanyanwu, S. Zhao, D. Soergel, C.-J. Hsich, I. Cai, S. Iqbal, M. Sundermeyer, Z. Chen, E. Bursztein, C. Malaviya, F. Biadsy, P. Shroff, I. Dhillon, T. Latkar, C. Dyer, H. Forbes, M. Nicosia, V. Nikolaev, S. Greene, M. Georgiev, P. Wang, N. Martin, H. Sedghi, J. Zhang, P. Banzal, D. Fritz, V. Rao, X. Wang. J. Zhang. V. Patraucean, D. Du, I. Mordatch, I. Jurin, I.. Liu, A. Dubey, A. Mohan, J. Nowakowski, V.-D. Ion, N. Wei, R. Tojo, M. A. Raad, D. A. Hudson, V. Keshava, S. Agrawal, K. Ramirez, 2. Wu, H. Nguyen, J. Liu, M. Sewak, B. Petrini, D. Choi, I. Philips, Z. Wang. I. Bica, A. Garg, J. Wilkiewicz, P. Agrawal, X. Li, D. Guo, E. Xue, N. Shaik, A. Leach, S. M. Khan, J. Wiesinger, S. Jerome, A. Chakladar, A. W. Wang, T. Ornduff, F. Abu, A. Ghaffarkhah, M. Wainwright, M. Cortes, F. Liu, J. Maynez, A. Terzis, P. Samangouei, R. Mansour, T. Kepa, F.-X. Aubet, A. Algymr, D. Banica, A. Weisz, A. Orban, A. Senges, E. Andrejezuk, M. Geller, N. D. Santo, V. Anklin, M. A. Merey, M. Baeuml, T. Strohman, J. Bai. S. Petrov. Y. Wu, D. Hassabis, K. Kavukcuoglu, J. Dean, and O. Vinyals. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context, 2024. httptttps///arivvv..t.
abs /2403 05530
[71] M. Thill, W. Konen, and T. Bäck. MarkusThill/GGBB The Mackey-Glass, Anomaly Benchmark. Zenodo, April 2020.
[72] L. Tran, L. Fan, and C. Shahabi. Distance-based outlier detection in data streams, Proceedings of the VLDB Endowment, 1089)1009-1100 2016.
[73] Y. Wang, X. Ma, G. Zhang, Y. Ni, A. Chandra, S. Guo, W. Ren, A. Arulraj, X. He, Z. Jiang, T. Li, M. Ku, K. Wang, A. Zhuang, R. Fan, X. Yue, and W. Chen. Mmlu-pro: A more robust and challenging multi-task language understanding benchmark, 2024. URL https
//arxiv. wwt./tg////////////e//o/oeeeeeet abs//4/b.../2.////////// 01574
[74] H. Wu, J. Xu, J. Wang, and M. Long. Autoformer: correlation for long-term series forecasting. In Proc. the Advances in Neural Information Processing Systems (NeurIPS), 2021.
Decomposition transformers with auto-
[75] H. WU, T. Hu, Y. Liu, H. Zhou, J. Wang, and M. Long. Timesnet: Temporal 2d-variation modeling for general time series analysis. In Proc. the International Conference on Learning Representations (ICLR), 2023.
[76] R. Wu and E. J. Keogh. Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress. IEEE transactions knowledge and data engineering, 35(3): 2421-2429, 2021.
[77] J. Xu, H. Wu, J. Wang, and M. Long. Anomaly transformer: Time series anomaly detection with association discrepancy. In Proc. the International Conference on Learning Representations (ICLR), 2022.
[78] N. Xu and X. Ma. LLM the genius paradox: A linguistic and math expert's struggle with simple word-based counting problems. In L. Chiruzzo, A. Ritter, and L. Wang, editors, Pro- ceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computationnal Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 3344 3370, Albuquerque, New Mexico, Apr. 2025. Association for Computational Linguistics. ISBN 979-8-89176-1888 189-66 URL /tttp:://acaanaaaaaa:
org/2025 naacl - 172/
[79] X. Xu, H. Wang. Y. Liang, P. S. Yu, Y. Zhao, and K. Shu. Can multimodal 1lms perform time series anomaly detection? arXiv preprint arXiv:2502.17812 2025.
[80] 7. Xu, A. Zeng, and Q Xu. Fits: Modeling time series with 10k parameters. In Proc. the International Conference Learning Representations (ICLR), 2024.
18

---

[81] Z. Xu, Z. Zhao, Z. Zhang, Y. Liu, Q. Shen. F. Liu, Y. Kuang, J. He, and C. Liu. Enhancing character-level understanding Ilms through token internal structure learning, 2024. URL https://arxiv org/ abs/2411. 17679
[82] T. Yairi, Y. Kato, and K. Hori. Fault detection by mining association rules from house-keeping data. In proceedings of the 6th International Symposium on Artificial Intelligence, Robotics and Automation in Space, volume 18, page 21. Citeseer, 2001.
[83] A. Yang, Y. Chen, S. Lee, and V. Montes. Refining time series anomaly detectors using large language models. arXiv preprint arXiv:2503.22) 218333 2025.
[84] C.-C. Ych, Y. Zhu, L. Ulanova, N. Begum, Y. Ding, H. A. Dau, D. F. Silva, A. Mueen, and E. Keogh. Matrix profile i: pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM), pages 1317-1322. Ieee, 2016.
[85] G. Yehudai, H. Kaplan, A. Ghandeharioun, M. Geva, and A. Globerson. When can transformers count to n? arXiv preprint wrXiv:2407.15111 2024.
[86] S. Zhang. Z. Zhong, D. Li, Q. Fan, Y. Sun, M. Zhu, Y. Zhang, D. Pei, J. Sun, Y. Liu, et al. Efficient kpi anomaly detection through transfer learning for large-scale web services. IEEE Journal Selected Areas in Communications, 40(8):2440-284 22450-2455, 2022.
[87] L. N. Zheng, C. G. Dong, W. E. Zhang, L. Yue, M. Xu, O. Maennel, and W. Chen. Revisited large language model for time series analysis through modality alignment. arXiv preprint arXiv:2410. 12326, 2024.
[88] H. Zhou, S. Zhang, Peng. Zhang. J. Li, H. Xiong, and W. Zhang. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proc. the AAAI Conference on Artificial Intelligence (AAAI), 2021.
[89] Z. Zhou and R. Yu. Can Ilms understand time series anomalies? In Proc. the International Conference Learning Representations (ICLR), 2025.
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A Related Work
A.1 Time-series Anomaly Detection
Problem Formulation. Let X1:7 (Z1...., .T) Rxx be time series sampled regular interval. Time-series anomaly detection seeks function f IR Txd - (SLT, Y1.7) that, given the entire sequence, returns a vector of continuous anomaly scores $1.7 E Rd and, optionally, binary label vector y1:7 {0, 1 obtained through a rule y/ l[s,, > T.. Each score 8, quantifies how strongly the statistical properties of observation æ; diverge from those expected under the normal generative process after accounting for trend, seasonality and stochastic boise-while the label flags the corresponding time point (or minimal enclosing interval) as anomalous when the divergence exceeds a threshold The objective is thus to isolate individual timestamps contiguous segments whose behavior departs significantly from the normal regime, signaling faults, rare events, or structural changes in the underlying system.
Within this formulation, we primarily consider two TSAD settings based on assumptions about label availability: unsupervised, where label information is available beyond the input time series, and semi-supervised, where only data known to normal is accessible. While certain existing methods 45 [9] incorporate anomaly information and thus deviate from these settings, we concentrate on the more realistic unsupervised and supervise scenarios. Approaches in this area have evolved from classical statistical techniques to modern deep learning and foundation models.
Statistical and Classical Models. Traditional methods such as STL decomposition 17 Matrix
Profile 844) and statistical thresholding (29] detect anomalies by explicitly modeling components like trend and seasonality. These approaches are efficient and interpretable but often struggle with
non-stationary and multivariate data.
Machine Learning-bas based Models. Classical unsupervised models include Isolation Forest [4]. Subspace PCA 21, and clustering- methods [82]. While these algorithms are lightweight and interpretable, they often rely on handerafted features and are limited in their ability to capture temporal dependencies.
Deep Learning-based Models. Deep models such as AutoEncoders 662.. USAD 7, and Omni- Anomaly 665 reconstruct normal patterns identify anomalies. More advanced architectures like the Anomaly Transformer [77] and TimesNet [75] leverage attention mechanisms to model long-term dependencies. These models achieve superior performance but require extensive training and tuning, and they often lack interpretability.
Time-Series Foundation Models (TS-FMs). Recent efforts like Chronos [6] TimesFM [18), MOMENT [24], and Lag-Llama (57] pretrain large models on diverse time-series datasets to enable zero-shot anomaly detection. These models show promise in generalization and transferability but still rely on scalar anomaly scores and thresholding for detection, limiting interpretability and precise localization.
Comparison Across Models. Table summarizes key differences:
Table 5: Comparison of unsupervised TSAD model families in terms of modeling capacity. training requirements, and output type.
Model Type
Temporal Modeling
Component Awareness
Domain Training
Output
Statistical
Low
Explicit
None
Score
ML-baaed
Moderate
Implicit
Light (generic heuristics)
Score
DL--hased
High
Implicit
Required
Score
TS-FM
High
Learned
Pretrained Pretrained
Score
LLM-based (ours)
Moderate
Structured (via prompt)
Label
Toward LLM-based TSAD. In contrast, Large Language Models (LLMs) offer novel paradigm
for TSAD by generating structured, interpretable anomaly reports through prompt-based natural
language reasoning 20 89). Rather than producing scalar anomaly scores, L.MSs can identify and explain anomalous intervals in plain text. However, existing LLM-based TSAD methods struggle
20

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with precise localization and temporal abstraction, especially when relying on visual inputs [99 or unstructured token prompts 33 43).
Our approach addresses these challenges by combining statistical preprocessing (e.g.. de- seasonalization) with index-aware prompting improve both detection and localization. This structured fusion of classical time-series principles and generative language reasoning establishes new direction for interpretable and flexible unsupervised TSAD.
A.2 LLM-based Time-series Anomaly Detection
Recent advances have explored the potential of using LLMs for TSAD, particularly in zero-shot or few-shot settings without domain-speciffe training. This line of research centers on a core question:
Can LLMs detect time-series anomalies without training? Studies [8 20 79
83 45 have
evaluated LLMs' ability to detect anomalies directly from raw or lightly preprocessed sequences via prompting. While some setups succeed in identifying simple anomalies, key limitations per- sist-namely, low detection accuracy, sensitivity to prompt variations, and hallucinations leading false positive cases. These investigations largely assess outcomes without examining the underlying causes of failure.
Vision-based anomaly detection with MLLMs. To bypass the difficulties LLMs face in capturing temporal patterns from textual input, some works (79) 89] propose converting time series into images and applying MLLMs interpret the visualizations. This approach shows robustness missing data and gross anomalies. However, it remains ineffective at detecting subtle irregularities hidden within complex, overlapping components. The transformation from sequence to image can also introduce distortions, particularly high-dimensional settings, leading to inaccurate or unstable predictions.
investigate context-aware strategies that incorporate in-context learning, AnoCoT, and domain priors improve both accuracy and interpretability. Although such methods enhance detection performance and support reasoning, they often rely on labeled anomaly examples during inference, which constrains their applicability to unsupervised semi-supervised TSAD scenarios.
Context-aware and interpretable detection. Other efforts 83 45
While earlier research has focused on whether LLMs can perform anomaly detection, our work shifts attention to understanding why they frequently fail. We identify two major barriers: (1) insufficient temporal abstraction and comprehension of latent components, and (2) structural misalignment between the input format and the task of localizing anomalies. Furthermore, we argue that visual representations alone are inadequate for precise localization, especially when positional indexing essential. Instead, we advocate for structured textual representations that maintain explicit reference to index positions. Finally, unlike prior methods that leverage known anomalies boost performance, show that excluding such priors and instead using contextual cues better aligns with realistic, unsupervised TSAD conditions. All evaluations are conducted on the AnomLLM benchmark [89 to ensure consistency and fair comparison.
B Benchmark Details
In this paper, we use two benchmark datasets. The first is the synthetic AnomL.LM benchmark, and the second is the real-world TSB-AD benchmark dataset. In this section, we describe the characteristics and statistical properties of each dataset.
B.1 AnomLLM benchmark
This appendix provides detailed information about the AnomLLM benchmark [89] datasets, which cover representative types of anomalies commonly discussed in time-series literature [16 52]. The original dataset consists of 8 datasets in total: point anomalies, range anomalies, trend anomalies, frequency anomalies, noisy point anomalies, noisy trend anomalies, noisy frequency anomalies, and flat trend anomalies.
Among these, the first 4 are basic data forms, and the latter 4 are noisy variant forms. We only used the first datasets, and we will provide an explanation of these datasets. shown the first three rows of Table 7 the four datasets differ in the presence or absence of the three components.
21

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Table Descriptions of the 21 prompt variants used in the AnomLLM benchmark. Prompts vary by supervision level (0-shot vs 1-shot), modality (text vs image), and auxiliary strategies such as CoT, statistical prefixing (PaP), arithmetic cues, and input formatting.
Code
Variant
Description
A
Ishot-vision-cot
One-shol prompt with visual input and chain-of-thoughi (CoT) reasoning guide step-by-ste step anomaly detection.
lslot-vision-cal
One-shot visual input with a correct arithmetic example test numeracy-
based reasoning.
C
Ishot-vision-dyscale
One-shot visual input with an incorrect arithmetic example to impair
numeric reasoning.
Ishot-vision
Basic one-shot visual prompt without CoT arithmetic guidance.
Oshot-vision-cot
Zero-shot visual imput with CoT prompting induce explicit anomaly reasoning.
F
Oshot-vision-calc
Zero-shot visual input with correct arithmetic example included.
G
Oshoo--ision-
Zero-shot visual input with incorrect arithmetic example test robust- ness mislcading signals.
Oshot-vision
Basic zero-shot visual prompt with only the time series image.
Ishot-text-sO.3-co.
One-shot text input (subsampled to s0.3) with a CoT reasoning trace.
Ishot-text-s0.3
One-shot prompt with s0.3 subsampled time series as plain text without reasoning.
Oshot-text-s0.3-ppp
Zero-shor text with Token- per-Digtt formatting to digit-level model- ing.
Oshot-text-s0.3-pp
Subsampled text with Prompt-as -reeffi: statistical summaries (mean, trend, etc.) precede the sequence.
M
Oshoot-eett=30.--dyyyaall
Suhsampled text with misleading arithmetic example to degrade arith- metic reasoning.
Oshot-text-sO,3-coV
Text input formatted as CSV (index, value) to test structured data han- dling.
Oshootteett=0033-coo--p
Combines CoT prompting with tokenized digit input for fine-grained reasoning.
Combines CoT reasoning with Prompt-as aaerrrs-PPrr---- statistical context.
Structured CSV input with CoT reasoning to examine logical behavior over tabular text.
Oshot-text-sO03-cco
Basic CoT prompt with subsampled text input (no format enhancements). Subsampled text with correct arithmetic example for numeracy- augmented detection.
Oshot-text-s0.--cale
T Oshot-text-sO.3
Plain zero-shot text prompt using s0.3 s0.3-length series in space-separated format.
Oshot-text
Full-Iengte zero-shot text prompt with raw values (space- separated.
In addition, [89 specifies appropriate values for minimum anomaly duration, normal duration, and other parameters for each dataset. These details are presented in Table Also, AnomLLM provided 21 prompt variants including COT. The descriptions for each variant are in Table 6. And also, the results of experiments using these variants are Table
B.1.1 Point Anomalies.
Normal data consists of periodic sine wave between -1 and 1. Anomalies present as noisy and unpredictable deviations from the normal periodic pattern, with frequency 0.03, normal duration rate 800.0, anomaly duration rate 30.0, minimum anomaly duration 5, minimum normal duration 200, and anomaly standard deviation 0.5. The example series is illustrated in Figure
B.1.2 Range Anomalics
Normal data comprises Gaussian noise with mean 0. Anomalies manifest as sudden spikes with values much further from 0 than the normal noise, with normal duration rate 800.0, anomaly duration rate 20.0, minimum anomaly duration 5, minimum normal duration 10, and anomaly size range (0.5, 0.8). The example series is illustrated in Figure 10
22

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Table 7: Comparison of anomaly statistics across different synthetic anomaly types.
Statistic
Point
Range
Trend
Frequency
Trend component
x
X
x
Seasonality component
/
X
Noise component
X
Time series
400
400
400
400
Samples per time series
1000
1000
1000
1000
Minimum anomaly duration
5
5
50
Minimum normal duration Average anomaly ratio
200
10
800
20
0.0320
0.0236
0.0377
0.0341
Time series without anomalies Average anomalies per series Maximum anomalies per series
117 (29.25%)
121 (30.25%)
230 (57.50%)
40 (10.00%)
1.17
1.20
0.42
2.16
Average anomaly length
27.26
19.73
88.61
15.77
Maximum anomaly length
165.0
113.0
200.0
111.0
600
400
800
1000
Figure 9: Example time series from the Point Anomalies dataset, with anomalies regions highlighted in blue.
Figure 10: Example time series from the Range Anomalies dataset, with anomalies regions highlighted in blue.
B.1.3 Trend Anomalies.
Normal data follows steady but slowly increasing trend from -1 to 1. Anomalies appear sections where the data increases much faster or decreases, deviating from the normal trend, with trend negation probability 50%, frequency 0.02, normal duration rate 1700.0, anomaly duration rate 100.0, minimum anomaly duration 50, minimum normal duration 800, normal slope 3.0, and abnormal slope range (6.0, 20.0). The example series is illustrated in Figure 11
200
506
803
102
800
400
200
300
1026
Figure 11: Example time series from the Trend Anomalies dataset, with anomalies regions highlighted blue.
B.1.4 Frequency Anomalies.
Normal data characterized by a periodic sine wave between -1 and 1. Anomalies occur as sudden changes in frequency, producing irregular periods between peaks, with frequency 0.03, normal
23

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duration rate 450.0, anomaly duration rate 15.0, minimum anomaly duration 7, minimum normal duration 20, and frequency multiplier 3.0. The example series is illustrated Figure 12
Figure 12: Example time series from the Frequnecy Anomalies dataset, with anomalies regions highlighted in blue.
B.2 TSB-AD Benchmark
This section describes the real-world dataset, TSB-AD benchmark [46], used in Section 4 The benchmark initially collected 13 univariate and 20 multivariate datasets. Following the curation process within TSB-AD, they obtained total of univariate and 17 multivariate datasets. Internally, datasets with dimension of were referred to as TSB-AD-U, among which we used the evaluation subset for our experiments.
To reduce the overall experimental cost, we restrict the evaluation set by selecting time series from eight categories (highlighted in green in Table within the TSB-AD-U benchmark, focusing those with relatively shorter lengths.
Table 8: TSB-AD-U evaluation benchmark dataset statistics. The total length is computed as the product the average time series length and the number of series (Count).
Dataset
Count
Dim
Total Len
Avg. # Anomaly
Avg. Len
Anomaly Ratio
Category
NEK
8
8,584
51.1
8.0%
P&Seq
TAO
2
20,000
838.7
1.1
9.4%
P&Seq
MSL
82
7
23,111
1.3
130.0
5.8%
Seq
Power
36
1
35,040
4.0
750.0
8.5%
Seq
Daphnnt
8
1
38,774
6.0
384.3
5.9%
Seq
YAHOO
39
30
45,270
5.5
2.5
0.6%
P&Seq
SED |
2
59,998
14.7
64.0
4.1%
Seq
TODS 38
13
65,000
97.3
18.7
6.3%
P&Seq
NAB 3
23
114,758
1.6
370.1
10.6%
Seq
Stock 72
8
130,000
1246.9
1.1
9.4%
P&Seq
SMAP 32
17
130,770
1.2
210.1
2.8%
Seq
CATSv2 21
348,639 1
300,000
19.0
778.9
4.9%
Seq
WSD 86
20
5.1
25.4
0.6%
Seq
SWaT 47
419,919 1
27.0
1876.0
12.1%
Seq
OPP 61
449,396 27
1.4
653.4
6.4%
Seq
MGAB
780,000
20.0
0.2%
Seq
SMD 65
791,393 8
2.4
173.7
2.0%
Seq
LTDB
800,000
127.5
18.6%
Seq
IOPS
1,0486822
25.6
48.7
1.3%
Seq
Exathlon 34
30
1,324,295
3.1
1577.3
11.0%
Sey
SVDB 25
20
3,016,800
36.4
292.5
3.6%
Sey
UCR 76
70
3,806,932
1.0
198.9
0.6%
P&Seq
MITDB 23
4,400,000 7
68.7
451.9
4.2%
Seq
C
Model details
C.1
LLMs
To evaluate whether our proposed strategies generalize across different LLMs, we conduct exper- iments using two open-source LLMss and two commercial API-based LLMs. For the open-source models, we employ InternV..2-Lamm33-766 and ( models, use Gemini- 1.iimi---5--Fii 5-assshhh by Google and GPT-4o by OpenAl.
For the API-based

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The open-souree models are hosted on an A100 4-GPU machine using the Imdeploy library, and queries are issued locally through this setup. For Gemini-1.5-Flash and GPT-4o, we directly send requests to their respective APIs without using multi-threading, in order to accurately measure per-query latency.
Intern-VLM 0S [4], also known as Intern is an open-source multimodal LLM designed to close the performance gap between open and commercial models in multimodal understanding. combines a powerful vision encoder, Intern with support for dynamic high-resolution processing (up to 4K), and bilingual training corpus. The language component is initialized with Hermes- 2-Theta-LLee LLaMA3-70B Intern VL2 achieves state-of-the-an results on 8 out of 18 benchmarks, outperforming some proprietary models in tasks like chart understanding.
In the AnOml.LM experiments, performance with and without visual input was assessed using a trivial image. The model achieved an MMLU-Pro (3) score of 52.95 without the image and 53.26 with the image. This suggests that visual input does not negatively affect language performance. also noted that the slightly lower MMLU score is attributable to the Hermes-based language backbone, which underperforms compared to Meta's official LLaMA3-70B-Instee
Qwen 19, specifically Dwen2.5-VL-72BB-1 Insttuuct, is large-scale multimodal language model developed by Alibaba Group. It builds upon the Qwen2.5 architecture and integrates a visual encoder to support image-tex. understanding tasks. The model features 72- 72-billion parameter transformer- based language backbone and leverages a high-resolution vision module. Pretrained on a mixture of web-scale bilingual corpora and diverse instruction-follwiin scenarios with multimodal inputs.
data, Qwen2.5-VL. is optimized for
Qwen2.5-VV VLL-7LB-IIntttnee demonstrated strong reasoning capabilities, maintaining consistent perfor- mance both with and without visual input. For example, achieved an MMLU-Pro score 71.2 [TO, compared to GPT-4o's 72.6. On the MathVision_Fust FULL benchmark, Qwen scored 38.1 while GPT-4o scored Suggessinn a slight advantage for Qwen when leveraging multimodal context. These results highlight the robustness of Qwen's visual- visual- language integration and training pipeline.
Gemini-L.5-Flash 770 is proprictary multimodal language model developed by Google, optimized for eigh-throughhutt and cost-efficient inference. It supports long-context processing and performs well across a wide range of multimodal tasks such as image understanding. classification, summariza- tion, and content generation from visual, audio, or video inputs. Despite its speed-oriented design, it maintains competitive quality comparable to other Gemini Pro models while significantly reducing operational cost.
In the AnomLLM study, small white image (10x10 pixels) was added to text prompts test whether including visual inputs would affect model performance. The MMLU-Pro score remained stable- 59.12 without image and 59.23 with indicaainn that the same model backend likely used regardless of input modality, and that vision input does not degrade text reasoning capabilities.
GPT-40 511. specifically the 4pt--40-2024-8--8 version, is OpenAT's latest flagship multimodal model that unifies vision and text understanding. Unlike earlier versions that relied on separate encoders for different modalities, GPT-4o adopts fully integrated transformer capable of jointly processing and reasoning across modalities with native support.
In the AnomLLM experiments, GPT-4o showed robust performance on both textual and multimodal benchmarks. Its MMLU-Pro score was 72.6 and GPQA score was 53.6. GPT-40 stands out for its balanced strength across diverse tasks and its ability seamlessly integrate visual context without requiring explicit prompting strategies or auxiliary modules.
C.2 Our strategies
Statistical time-series decomposition. To compare with L.LM-based TSAD approaches, we con- ducted experiments using conventional statistical decomposition techniques. Specifically, we em- ployed the seasonal, decompose _eassonal,decompsse function from the statsmodels package, which decomposes a univariate time series into three components: trend, seasonality, and residual. This method assumes that the observed signal consists of systematic patterns combined with random noise, and supports both additive and multiplicative models. Following prior work (46), we adopt the additive model, defined as:
Y(t T[t S(t] + e(t).
25

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where [t) is the observed value at time t, [[] is the trend component, S(t] is the seasonal component, and e|t] is the residual.
The decomposition process uses moving averages to isolate the seasonal component, with the period either inferred from the time index specified manually. When two sided=True, centered moving average is used; when False, the filter is causal, relying only on past values.
This method yields structured separation of the time series into long-term trends, recurring seasonal patterns, and residuals. Anomalies are detected by analyzing the residuals, which capture deviations unexplained by the expected structure.
Our prompts. our experiments, we conducted tests using both Texot-Text and Oshot- Vision while providing the corresponding indexes. We used the prompt shown in Figure 13 for these experiments.
Our (Oshot-Text- -exii-)
I will provide you with time-series data recorded at hourly intervals, along with plotted time-serics image. Here is time-series data in (index, value) format:
<history>
(index, value)
(index, value)
(index, value)
</history>
Assume there are up to anomalies.
Detect ranges of anomalies in this time series, in terms of the index of time-series data, considering the plotted image.
List one by one, JSON format. If there are no anomalies, answer with an empty list D. Do not say anything other than the answer.
Response: ((start, end), (start, end).
Our (Oshot-Text)
I will provide you with time-series data recorded at regular intervals.
Here time-series data (index, value) format:
<history>
(index, value)
(index, value)
(index, value)
</history>
Assume there are to anomalies.
Detect ranges of anomalics in this time series, in terms of the index of time-scries data. List one by one, in JSON format. there are no anomalics, answer with an empty list []. Do not say anything other than the answer.
Response: [(start, end), (start, end),
Figure 13: The prompts used in the experiments: Oshot Text- Vision and Oshot Text.
26

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C.3 Conventional non-LLM-based TSAD Methods in Section 5.1
ii) ML baseline
SR [59] first applies the Fourier Transform to the input data and then computes the spectral residual from the log- amppliude spectrum. This modified spectrum is converted back to the time domain using the Inverse Fourier Transform, resulting in saliency
map. Anomaly scores are derived by measuring how much each value in the saliency
map deviates from its moving average.
Forest 44 builds binary tree where the length of the path from the root to given node indicates the likelihood of an anomaly- paths imply higher chance that
the point is anomalous.
Sub-PCA
projects subsequences into a lower-dimensional subspace, and large deviations from this projection space are treated as anomalies due to the breakdown of linear assumptions.
2
KMeans-AD
82 assigns each subsequence to the nearest cluster centroid using k- means and then calculates anomaly scores as the distance between the subsequence and its assigned centroid.
MatrixProfile [84] detects anomalies by identifying subsequences with unusually large distances their nearest neighbors, thus uncovering patterns that differ from the rest of the time series.
(ii) DL baseline
CNN 40 trains convolutional network to predict future time steps from recent observations, and the anomaly score is derived from the prediction error at each point. USAD 7 combines reconstruction and adversarial loss from two autoencoders, where discrepancies between input and reconstruction, amplified by discriminator, yield the anomaly score.
Anomaly Transformer [77] introduces an Anomaly-Attento mechanism that captures temporal dependencies and assigns anomaly scores based deviations in attention- based associations.
OmniA nomaly (65] uses a stochastic recurrent neural network with variational infer- and normalizing flows to learn representations of normal patterns, identifying anomalies via reconstruction probabilities.
AutoEncoder (62 projects vector to the power-dimensiona latent space and reconstruct phase, where anomalies are typically characterized by evident reconstruction deviations.
it through the
NemesNet [75] adopts a ceneral-purpost block for multivariate time series that adap- tively learns multiple periodicities via hierarchical temporal convolutional structure. FITS [80] interpolates time series into the complex frequency domain to detect anoma- lies, leveraging the efficiency of frequency-domain manipulation with minimal parame-
ters.
(iii) Foundation Model-based Method
Chronos 6 tokenizes time series values through scaling and quantization into discrete tokens and trains a T5 model using a standard cross-entropy loss for anomaly detection.
TimesFM [18] pretrains a decoder-style attention model using input patching on a large-scale time series corpus to learn general representations for various forecasting and detection tasks.
Lag-Llama [57] introduces a decoder-only transformer architecture that conditions on lagged inputs, making it suitable for probabilistic forecasting and anomaly detection on univariate time series.
MOMENT 24 is a T5-based encoder model pre-trained using masked time-series modeling reconstruct masked values, enabling fine-tuning for downstream anomaly detection tasks.
(iv) Thresholding Method
Precentile 6 selects a fixed quantile (e.g., top 5%) of the anomaly scores as the threshold to label outliers, assuming the score distribution reflects normal vs. abnormal points.
27

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MAD 29 computes the median absolute deviation to identify outliers, offering robust- ness against extreme values and non-Gaussian distributions.
cs 67 fits the tail of the anomaly score distribution with the Generalized Pareto Distribution via Peaks Over Threshold, enabling dynamic anomaly thresholding.
EVT-POT
D Experiment details
D.1 Understanding experiments in Section 2.1
Determining whether anomaly exists
Determine whether this time series contains any anomalies. Respond with exactly 'True' if anomalies are present, 'False' otherwise. Output only "True' or 'False'.
Response: 'Truc' or 'False'
Figure 14: prompt that determines the presence of anomalies and outputs binary result.
To evaluate how well LLMs understand different types of anomalies in the TSAD setting, we design an experiment using datasets that cover four representative anomaly types: trend shift, frequency change, context-deviating point, and out-of-range anomalies, as proposed AnomLLM. The dataset consists of 1,600 time series samples, with 400 instances per anomaly type. We convert the original interval-basee labels into binary labels by marking whether any anomaly interval is present given time series, effectively framing the task as instance-level TSAD without requiring localization. Accordingly, the prompt output format modified from interval prediction to binary decision, as illustrated in Figure 14
The experimental setup follows the same configuration as the TSAD task in AnomLLM, with the only modification being the binary output format. We adopt the F1-Macro score to fairly assess performance across both normal and anomalous classes. As lower-bound reference, we include constant classifier baseline to delineate the threshold for meaningful predictions. The experiment leverages the 21 prompt variants proposed in A with detailed descriptions provided in Table 6 and full dataset statistics presented in Table 7
D.2 Localization experiments in Section 2.2
Ushot-Vision prompt for image sequences with ground truth labels
Detect ranges of anomalies, highlighted by blue-colored regions, in this time series, in terms of the x-axis coordinate.
List one by one. in JSON format. If there are no anomalies, answer with an empty list [].
Output template:
"start": number, "end": number .....
Response:
[[ "start": number, "end": number ......
Figure 15: The Oshot-vision prompt for localization-only experiment.
This section provides detailed information about the experiment designed to evaluate how well the model localizes anomalies during TSAD using AnomLLM. The dataset and evaluation protocol follow the AnomLLM benchmark. To minimize performance degradation caused by failures in understanding, we explicitly provide the ground-truth anomaly segments during the detection process for each sample as shown in Figure 9
Specifically, this experiment uses the 0-shot vision-based prompt, which demonstrated strong per- formance in the original AnomLLM benchmark. When presenting the image, the ground-truth
28

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anomaly segments are visually highlighted within the image to clearly indicate the anomalous regions. TSAD then performed using prompt (Figure 13) information.
that explicitly instructs the model to utilize this
D.3
Counting experiments in Section 3.2
Counting prompt
Below numerical sequence:
deelectedsseries
Count the total number of elements in this sequence. Please
return only the count single integer without any
additional text
explanation.
Response: integer
Figure 16: The prompt used in the experiment to count elements in given sequence.
This section offers additional details on the experiment introduced earlier. First, we conduct a sequence-length counting task by randomly sampling 100 sequences from the AnomLLM benchmark. Sequence lengths range from to 1,000 and are binned into ten 100-step intervals (1-100, 101-200, .... 900--00,,,,,, with exactly ten sequences drawn from each interval ensure uniform distribution. We test four large language models and four different separator tokens used to delimit values within a sequence. Each model is instructed return single integer representing the sequence length. As shown in Figure [6] The prompt template is identical to the one adopted in Oshot-text variant of AnomLLM, except that the desired output replaced with the sequence length.
D.4 Time-series decomposition experiments in Section 4.2
This section provides further details on our evaluation of LLMs' ability to perform time-series decomposition without relying on external tools. We consider two subtasks: (1) identifying whether time-series contains trend and/or seasonality components, and (2) generating the corresponding component sequences.
For the detection task, we constructed prompts that directly asked whether the given time-series
contains trend seasonal structure. Example prompts are provided in Figure 17 We used synthetic sequences with known decomposition structure sampled from AnomLLM as ground truth. As baseline, we apply statistical decomposition and threshold to determine whether each component is present in time series. The algorithm is shown in Algorithm T Specifically, a component is considered present if its amplitude exceeds a certain percentage of the original series' value range. For example, with threshold set to 10%. we compute the range of the original time series (i.c.. max minus min), and if the amplitude of the seasonal component exceeds 10% of this range, we classify the series as containing seasonality. This threshold-basee rule transforms the continuous decomposition output into a binary detection result. comparison of F1 scores across models presented in Figure a)
Specifically, we used the following hyperparameters.
thresh_trend-10
Frres__saasnal-O..
thresh_resid-D
For the generation task, models were asked output the trend and seasonality as separate numerical sequences given the original input. Prompt examples for this task are shown in Figure [7] While some responses preserved global shape, most models failed to accurately reproduce the true components. In particular, emoor increased with sequence length, and artifacts such as baseline drift and amplitude attenuation were frequently observed. comparison mean absolute errors across models presented in Figure b), and representative examples are illustrated in Figure/(c))
29

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Component detection prompt
Time series data can typically decomposed into three main components such as Trend, Seasonality, and Residuals.
In this analysis, Residuals are further divided into Noise and Anomalies.
The four components exhibit the following characteristies:
Trend: A long-term directional pattern or consistent upwarddownward movement. Seasonality: Regular and predictable cycles repeating at consistent intervals. Residuals: The remaining noise or anomalies in the data.
Below the given time series data: [time ]eries]
Analyze the provided time series earefully. Determine if each of these components (Trend, Scasonality, Residuals) is present absent (0).
Provide your answer strictly in the following JSON format: ("Trend":
0 or 1, "Seasonality":
0 or 1.
"Residuals":
or 1)
Response:
""Trend"; or "Seasonality": Oor 1, "Residuals": or
Component generation prompt
Time series data can typically he decomposed into three main components: Trend, Seasonality, and Residual.
The three components exhihit the following characteristics: Trend: A long-term directional pattern or consistent upward/downward movement.
Seasonality: Regular and predictable cycles repeating consistent intervals. Residual: The remaining noise or anomalies the data.
Below the given time series data: "sampled_scries,
CRITICAL INSTRUCTION: Extract ONLY irend component from this time series.
EXACT LENGTH REQUIREMENT: Your output MUST contain EXACTLY (num_samples) values one trend value for EACH value in the original time series.
DECIMAL PRECISION: Maintain the same level precision as original data decimal places). Example: If original values are like "-0.63", "-0.57", trend values should also have 2 decimal places like "-0.62", "-0.61".
Count the values in the original data carefully. The original has exactly (num samples) values, so your trend component must have exactly num samples values, no more and no
Return your answer of [component] as spaco-scparated numbers in the following JSON format:
"(component)" "; "valuel value2 value3 ... value (aamennummmaaaaeee "um__amples)"
DO NOT abbreviate or shorten the output. Include EVERY single trend value with 2 decimal places.
Response:
"(component]" ": "valuel value2 value3 ... value haaeenummlmlleeeee
Figure 17: Component detection and generation prompts used in the decomposition experiment.
D.5
Index-free vs. Index-aware experiments in Section 4.2
Prior works have often favored the index-free approach, as it requires only a simple list of values that compose the sequence, resulting short and concise format. This simplicity can make it easier for LLMs understand the sequence, potentially contributing positively to task performance. However,
30

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Algorithm Component Detection in Time Series
Require: Time series sequence, seasonality period, Threshold values Uhresharends srasonal
threshyesid
Ensure: Binary indicators for trend, seasonal, and residual components
function DETECCCCOPPOENTSSGes) period, threshurend: thresh threstyresid)
2: decomposition & ScasonalDecomposee (seq, model = "additive", pperood = period)
3: tr - decompositionn.rren
4: se < decomposition.seassmn
5: re decomposition.
6:
Per < 1 (maxx [tr]) > thresherend)
Binary indicator for trend Binary indicator for seasonal
7: Pse f- 1 mmax se (hreell)essonal)
8: Pre 1 (max (Iel)) threshresid)
>. Binary indicator for residual
9: return Ptr: Pse. Pre
10:
end function
TSAD with GT labels with text sequence only (Index-aware)
(968,-0.03), (969,-0.1), (970,0.12), (971,0.1), (972,0.24), (973,0.11), (974.0.03), (975,-0.02), (976,0.09), (977,0.14), (978,-0.0) -7003. (979,-0.01), (980,-0.18). (981,0.06), (982,-0.0), (983,0.14), (984,0.74), (985,0.73), (986,0.78), (987,0.56), (988,0.98), (989,0.78), (990,0.7), (991,0.8), (992,0.64), (993,0.82), (994.0.69), (995,0.55), (996,0.51), (997,0.9), (998,0.78), (999,0.58)
Assume there up anomalies. Detect the ranges of anomalies in this time series based the x-axis coordinate, where the x-axis coordinate refers to x value (x, y) data. The patterns anomalies are as follows.
anomaly data 1: 0.07 -0.4 -0.71 -0.8 -0.58 -0.46
anomaly data 0.12 0.46 0.75 0.7 0.82 0.65
anomaly data 3: 0.14 0.74 0.73 0.78 0.56 0.98 0.78 0.7 0.8 0.64 0.82 0.69 0.55 0.51 0.9 0.78 0.58 List one by one, in JSON format. If there anomalies, answer with an empty list [].
Output template: [[
start": "end": ...), ("start": .... "end": ...)...]
Response: json ("start": 414, "end": 460), ("start": 215, "end": 221 J. ("start": 984, "end": 999)
Figure 18: A successful case in querying index-aware prompts for text-only TSAD with ground-trus truth labels.
in TSAD, the absence of index information introduces significant drawback: when models are required to output anomaly intervals, they must internally perform a counting task to determine positions.
In contrast, the index-aware approach explicitly includes index-value pairs, requiring additional separator tokens and leading to longer and more complex input formats. This added complexity can potentially degrade task accuracy, especially when the context window becomes too long.
To evaluate the trade-offs between these approaches, conducted experiment designed to isolate the effect of index formatting. To control for other variables, we only used text-based sequences without any accompanying image sequences. Similar to the localization experiment in Section 2.2 we provided ground-truth anomaly labels and compared localization performance under two conditions: with and without index information.
When using only text sequences, we included the ground-truth anomaly segments in the prompt and designed the task such that the model had to identify these anomalies in the sequence shown in Figure I8
As result, consistent with our earlier discussion, we found that even though the index-aware prompts were longer and more complex, they outperformed index-free prompts. The benefit of eliminating the need for internal counting outweighed the cost of increased input length. In the case of Qwen,
31

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Unnecessary code generation and reasoning outputs when using index-free prompts.
Response: To detect anomalies, we compute the mean (μ) and standard deviation (a) of the series, and declare any point more than 3a away from y an anomaly.
Steps:
1. Compute the mean (1)).
2. Compute the standard deviation (a).
3. Mark points further than 3a from μ anomalies,
Let's calculate these values and identify the anomalies.
#*## Calculations:
1. **eaan (p)**: The average value of the dataset.
**Standard Deviation (a)**; A measure of the amount of variation or dispersion of set of values.
python import numpy as np
data [0.05, -0.02, -0.06, -0.01, 0.14, 0.12, -0.04, 0.05, -0.04, 0.1, 0.03, 0.02, 0.1, -0.0, 0.1, -0.06, -0.01, 0.09, -0.1,
mean = (damean(data)
std_dev = (dtat
anomaly_threshoole = * std dev_
anomalies []
for i, value in enumerate(data):
if abs(value mean) > anomaly thresholdd
mnomalles.appenddff
Grouping anomalies into ranges
anomaly_range pranges []
if anomalies:
start anomalies[0] for in range(1, len(anomalies)):
if anomalies[i] != anomalies(i 1] 1:
anomaly_ amomaly_ranges.app wanngss.append"sttae "sttatt:: start, "end": anomalies[i IJ)
start = anomalies[i]
anomaly ranges..ppend(( "startt:. start, "end": amomalies[-1]))
print(anomaly ranges)
### Output: "'json (" ("start:: 18, "end": 18), ("start": 25, "end": 25), ("start": 27, "end": 27).
Figure 19: A failure case querying index-free prompts for text-only TSAD with ground-truth labels. The quantitative evaluation results of the index-free and index-aware prompts are presented in Section 4.2
index-free prompts occasionally resulted in the generation of unnecessary code or reasoning steps (Figure 19), suggesting that the inherent difficulty of the task without index cues may responsible for such verbose outputs.
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D.6
Context-aided TSAD experiments in Section 5.2
Prompt and Response of ETTh2 example
I will provide you with time-series data recorded at hourly intervals, along with a plotted time-series image.
Here is time-series data in (timestamp, value) format: <history>
(2016-08- 01:00:00, 46)
(2016-08-22 20 02:00:00, 45)
(2016-08-20 03:00:00, 44)
(2016-09-11 08:00:00, 34)
(2016-09-11 09:00:00, 35)
</history>
Here is context to understand the time series.
following event occurred given period. time-series pattern that can be explained by this event not considered an anomaly.
A flat time-series pattern may simply result from handling missing values caused by system shutdowns.
Assume there are up to anomalies.
Detect ranges of anomalies, which are not explained by context in this time series, in terms of the timestamp of time-series data, considering the plotted image.
Stept) Present possible anomalics with reason.
Step2) Remove explainable anomalies by context.
Step3) List one by one, in JSON format. there are no anomalies, answer with an empty list [. not say anything other than the answer. Output template: [("s (("start timestamp": "end timestamp": ...), ("start timestamp": .e.,,"nn "end timestamp":...h
Please answer all steps.
Response:
Stepl) Present possible anomalies with reason.
From 2016-08-31 00:00:00 to 2016-08-31 23:00:00, the time series shows a pattern at a value of 33, which could be considered an anomaly due to its deviation from the usual pattern of fluctuations.
2. From 201 2016-09-06 22:00:00 to 2016-09-07 08:00:00, the time series shows sudden drop lower value range. which could considered anomaly due to its deviation from the usual pattern.
Step2) Remove explainable anomalies by context.
1. The flat pattern from 2016-08-31 00:00:00 to 2016-08-31 23:00:00 which mentions that flat patterns may result from handling missing values due system shutdowns. Therefore, is not considered an anomaly.
be explained by the context provided,
2. The sudden drop from 2-16-09-06 22:00:00 to 2016-09-07 08:00:00 does not have an explanation provided by the context, remains potential anomaly.
Step3) List one by one, in JSON format.
"json [[" (taat timestamp": "2016-09-06 22:00:00", "end timestamp": "2016-09-07 08:00:000000000
Figure 20: The prompt and response of qualitative result on (b) potential range anomalies in ETTh2.
Conventional TSAD has relied heavily on numerical sequences, using statistical or neural network- based models. While there have been attempts to incorporate textual information into these models, such approaches often depend on synthetic datasets due to the difficulty of collecting paired text
33

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and time-series data. As result, they fall short in terms of generality and reasoning capabilities compared to LLMs. Thus, without other modality, these conventional models generally depend on an assumption in which anomalies are defined by sparsity or abrupt changes in the time-series patterns.
However, in real-world applications, not all sparse abrupt patterns of actual concern. For instance, in e-commerce platforms, promotional events can cause a sudden surge in product sales, Traditional models may flag such change an anomaly, despite it being an expected and well- known outcome. While it is possible to exclude known time windows using rule-based filters, this quickly becomes aabor-intensive and inflexible when many events or patterns need be handled. In contrast, simply describing these contexts in natural language would be a far more scalable and user-friendly solution.
In this situation, the reasoning ability of LLMs to interpret natural language context offers a valuable control interface for TSAD. To explore this capability, conducted study using real-world Aatasets-ETTh2 and Electricity. We first ran eight conventional anomaly detection models on these datasets and extracted time segments that were commonly detected as anomalies. We then generated domain-informed contextual descriptions for each of these segments, simulating plausible scenarios such known event causing sudden drop, or a specific pattern being aligned with external factors. These contexts provided time-related or value-specific cues in natural language.
Using the prompt as shown in Figure 20 evaluated the anomaly decisions of the LLM both with and without these contextual imputs. Our results show that the LLM could effectively integrate tempo- ral cues, numerical patterns, and contextual knowledge to suppress false positives-i.e., anomalies detected by conventional models that users would not consider problematic.
Unlike conventional models that rigidly detect anomalies based on pattern shifts alone, Maaeed context-aided TSAD can reinterpret these shifts in light the user's intent and domain knowledge. This allows for "justifiable detection," where the LLM not only detects changes but reasons whether those changes are relevant anomalies given the context. The LLM thus transforms anomaly detection into interactive, user-aligned process that prioritizes interpretability and flexibilityy.
This paradigm shift points to several promising directions for future work: improving generalization across domains, ensuring real-time applicability of context-awand detection, and defining minimal requirements for effective contextual prompts. Notably, because the meaning of "anomaly" can vary depending on the user's objective or operational context, LLMs open the door to a new class of objective-awar anomaly detection systems-systems that adapt dynamically changing definitions of what matters, rather than adhering to a fixed statistical criterion.
34

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Table TSAD results on the evaluation set of TSB-AD-U benchmark. The summarized results are
presented in Section 5.1
Standard Metrics
Affiliation Metrics
Inference Time
Method
Thresholding
Prec.
Recall
F1
Prec.
Recall
F1
A (GPT-40)
12.79
11.96
9.32
39.59
30.54
31.59
25.52
Anomaly Transformer
Percentile
15.21
40.35
13.91
54.04
86.67
64.13
8.49
FITS
MAD
13.88
38.54
16.54
56.03
86.48
65.73
4.05
TimesNet
MAD
14.40
39.63
17.71
56.87
85.48
66.02
5,91
MatrixProfile
MAD
19.73
28.82
18.29
61.87
59.30
54.80
33.58
MOMENT-ZS
Percentile
26.07
27.35
20.42
65.33
71.04
63.82
8,90
OmniAnomaly
MAD
27.91
30.81
20.46
63.14
83.35
68.08
2,66
KMeansAD-U
Percentile
31.58
25.28
22.14
66.57
50.01
50.77
1.28
AutoEncoder
MAD
27.12
27.44
22.27
66.21
60.82
57.25
3.16
Lag-Llama
MAD
24.17
45.67
23.54
65.20
92.70
74.54
118.85
Sub-PCA
MAD
26.55
28.00
23.72
62.37
54.31
54.74
0.18
USAD
MAD
23.92
34.54
24.46
59.84
61.60
55.24
1.57
TimesFM
MAD
25.47
$2.18
24.98
66.32
94.65
75.72
130.09
Chronos
MAD
23.58
55.65
25.08
64.87
96.33
75.90
69.10
IForest
MAD
33.99
26.62
26.79
68.24
68.44
64.72
0.52
SR
MAD
32.61
40.77
30.57
66.80
95.11
75.94
0.02
Our (GPT-4o)
47.60
31.88
34.20
76.10
81.49
74.64
140.63
E
Full benchmark results
Gemini-1.5-Flan
Standard
Affiliation
Datasets
Code
Prompt
Prec.
Recall
F1
Prec.
Recall
F1
A
Ishot-vision-cot
41.07
40.88
39.55
50.91
53.18
51.82
Ishot-vision-calc
63.33
63.63
62.75
67.09
67.43
67.25
sshot-vision-dyscale
62.01
62.22
61.86
64.12
64.37
64.24
Ishot-vision
57.59
57.73
56.75
62.41
62.81
62.59
Oshot-vision-cot
57.93
57.94
57.93
57.99
58.00
$7.99
Oshot-vision-cale
59.70
59.44
59.48
60.15
60.20
60.18
Oshot-vision-dyssaale
59.08
59.03
58.95
59.43
59.47
59.45
H
Oshot-vision
59.77
59.57
59.60
60.17
60.22
60.19
Ishot-text-s0.3-co
5.64
7.96
6.32
19.15
24.30
21.23
Ishot-text-s0.3
2.03
2.15
1.88
11.72
17.19
13.70
Oshot-text-s003--pp
0.00
0.00
0.00
3.03
5.81
3.97
L
Oshot-text-s0.33
0.00
0.00
0.00
3.37
7.25
4.56
0.00
0.00
0.00
3.90
7.85
5.20
Trend
N
Oshot-text-s0.3--o
0.00
0.00
0.00
3.15
7.16
4.31
Oshhot-tett-s03--co0-1
3.25
3.25
3.25
7.83
10.62
8.88
P
Oshot-text-s0)3-cort
0.25
0.25
0.25
3.26
5.66
4.07
0shoot-eettsl)33-cottcvv
0.50
0.50
0.50
5.51
9.70
6.88
R
Oshot-text-s0.3-ee
0.52
1.01
0.63
7.83
13.90
9.94
Oshot-tcxt-s0.3-ee
0,00
0.00
0.00
4.59
8.98
6.06
T
Oshot-text-s0.3
2.12
2.58
2.07
12.28
17.76
14.31
Oshot-text
0.00
0.00
0.00
3.99
7.79
5.27
Our
Oshot-text
65,38
61.87
62.52
67.47
70.49
68.28
Our
Oshot-text-vision (w/o value)
70.87
71.07
70.30
88.41
91.37
89.62
Our
Oshot-text-vision (w/o index)
74.95
75.70
74.38
85.63
86.40
85.95
Our
Oshot-text-vision (w/o deseason)
58.76
58.45
58.47
59.69
61.47
60.27
Our
Oshot-text-vision
82.90
81.81
81.28
90.08
92.70
91.11
A
Ishot-vision-cot
14.29
16.89
13,80
54.19
41.68
43.93
Ishot-vision-cale
23.10
20.50
20.73
34.76
24.87
27.46
C
Ishot-vision-dyscalc
23.73
21.83
21.61
36.35
24.37
27.57
35

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D
Ishot-vision
21.78
21.31
20.52
35.84
25.89
28.46
Oshot-vision-cot
12.65
12.71
12.45
14.16
12.52
12.98
F
Oshot-vision-calc
17.82
19.68
18.14
23.65
17.80
19.39
Oshot-vision-dyssalc
16.71
17.84
16.83
21.44
16.58
17.88
H
Oshot-vision
13.56
13.77
13.54
15.47
13.15
13.76
-shot-text-s0.3-cot
12.81
17.30
12.31
56.67
50.63
51.30
Ishot-text-sO.3
10.20
14.73
10,45
56.37
52.78
52.61
0shot-text-3003-pp
2.57
1.57
1.63
39.54
29.61
32.22
Oshot-text-sO.3-pag
1.97
0.73
0.94
33.36
22.71
25.54
Oshot-text-s00.
3.74
3.24
2.91
36.95
28.19
30.32
N
Oshot-text-sO.3-44
2.77
2.21
2.08
46.48
33.66
37.03
0shoo-teett=0033-cott--
2.61
2.37
1.97
25.96
19.14
21.00
0shoo-teett-s0.3-cot--p
3.92
2.35
2.61
29.46
19.21
22.12
Q
0shoo-textt:003--o0---
2.55
2.34
1.94
30.81
23.37
25.37
Oshot-text-s003-coo
3.46
4.78
3.36
21.76
19.93
19.99
Oshot-text-s0.3-cale
3,29
2.61
2.51
36.76
27.58
29.91
0shot-text-sO.33
11.28
14.24
10.91
58.42
52.67
53.59
Oshot-text
4.01
4.30
3.63
39.24
31.84
33.65
Our
Oshot-text
37.92
24.04
25.75
62.71
52.37
54.95
Our
Oshot-text-vision (w/o value)
30.17
50.64
33.71
80.53
70.80
73.25
Our
Oshot-text-vision (w/o index)
30.20
45.50
32.56
77.49
67.14
69.69
Our
Oshot-text-vision (w/o deseason)
48.87
33.96
36.87
55.99
41.00
45.47
Our
Ostot-text
46.23
47.13
39.98
81.57
70.16
73.03
A
Ishot-vision-cot
43.09
67.37
49.31
82.62
83.34
82.61
Ishot-vision-calc
55,95
77.36
61.54
94.88
94.55
94.17
Ishot-viison--yysale
54.29
78.90
60.44
95.18
96.15
95.25
D
Ishot-vision
53,54
78.76
59.96
93.42
93.83
93.19
Oshot-vision-cot
51.87
84.03
59.20
91.78
89.43
89.75
F
Oshot-vision-cale
51.56
87.56
59.74
94.28
95.88
94.63
Oshot-vision-dyssall
51.40
88.72
59.53
94.18
95.87
94.52
H
Oshot-vision
52.35
90.69
60.90
94.22
95.46
94.38
1shot-text-s0..-co
8.19
9.11
8.05
45.60
43.79
43.26
1shot-text-s0.3
3.63
4.20
3.61
43.49
41.13
40.93
Oshot-text-s003--pd
1.83
1.76
1.77
22.66
20.71
20.94
L
Oshot-text-s003-puug
0.00
0.00
0.00
15.58
14.10
14.28
0shho--eett:00.--dyysal
0.20
0.10
0.12
18.22
18.78
18.08
Point
N
0shot-text-s0.3-css
3.81
2.85
2.74
34.27
29.22
30.44
Oshot-text-ss.3-cootte
3.89
3.79
3.76
18.41
16.85
17.14
-shot-text-s0.3-cot
3.31
3.51
3.30
17.68
16.21
16.44
Q
Oshot-text-s0.3-ccom
2.88
2.86
2.19
24.03
20.64
21.46
Oshot-text-s003-cot
2,66
3.72
2.54
19.37
20.22
19.12
Oshot-text-s0)3-call
0.10
0.05
0.06
18.67
19.37
18.53
Oshot-text-s0.3
2.75
3.60
2.83
43.01
41.02
40.64
U
Oshot-text
1.09
1.06
0.93
23.84
24.38
23.35
Our
Oshot-text
67.99
35.56
43.39
69.75
67.17
67.79
Our
Oshot-text-vision (w/o value)
56.52
81.42
62.76
95.86
95.95
95.52
Our
Oshot-text-vision (w/o index)
57.21
76.78
62.52
93.78
96.26
94.54
Our
Oshot-text-vision (w/o deseason)
87.85
88.36
85.44
98.91
97.12
97.64
Our
Oshot-text-vision
90.89
74.93
78.41
98.45
96.81
97.11
Ishot-vision-cot
22.85
50.51
28.87
69.16
71,62
70.05
Ishot-vision-calc
36.28
64.25
42.40
81.41
84.05
82.40
Ishot-vision-dyscale
35.52
61.50
41.15
81.49
83.06
81.82
D
Ishot-vision
33.77
63.17
40.02
80.05
82.67
81.07
Oshot-vision-cot
28.58
61.32
34.67
75.91
73.62
73.75
F
Oshot-vision-calc
20.96
60.42
28.02
68.04
70.94
69.07
Oshot-vission-yssall
26.94
66.70
34.00
74.17
77.29
75.34
H
Oshot-vision
32.70
74.46
40.19
79.35
83.16
80.95
36
Range

---

Ishot-text-sO.3-com
12.04
14.57
12.31
46.38
49.43
46.57
1shot-text-s0.3
7.12
13.71
8.42
47.06
56.95
50.69
K
Oshot-text-sO.3--pp
2.30
2.63
2.13
29.22
28.56
27.96
Dshot-teett:s033ppp
1.97
0.88
1.08
30.66
27.80
28.09
M
Dshot-teetts0).--dysslll
2.20
1.95
1.85
33.94
36.51
34.22
Oshot-text-3003-eeS)
5,00
4.52
4.62
35.17
33.31
33.28
Oshot-text-sO.3-coot
2.87
3.03
2.86
16.75
14.98
15.26
Oshot-text-s0.3-cott
6.59
6.17
6.17
25.46
22.12
23.01
Oshot-text-s0.3-cot--
2.70
2.24
2.34
16.85
15.47
15.75
Oshot-text-s0.33
4.28
4.90
4.18
20.54
22.27
20.84
Schot-text-s0.--caan
2.24
1.92
1.81
35.13
36.94
34.99
Oshot-text-s0.33
7.15
12.76
8.34
46.77
56.31
50.25
U
Oshot-text
2.92
4.53
2.98
32.52
38.30
34.27
Our
Oshot-text
59.97
53.65
54.81
64.74
67.29
65.47
Our
Oshot-text-vision (w/o value)
38.54
67.64
44.81
83.56
86.21
84.57
Our
Oshot-text-vision (w/o index)
20.27
49.77
26.97
61.38
68.83
64.52
Our
Oshot-text-vision (w/o deseason)
54.54
61.60
55.60
66.94
68.64
67.60
Our
Ostot-text-vision
53.15
59.37
53.57
66.36
68.09
66.96
Standard
Affiliation
Datasets
Code
Prompt
Prec.
Recall
Prec.
Recall
F1
Ishot-vision-cot
44.90
44.90
44.29
46.92
47.32
47.08
Ishot-vision-calc
33.76
39.43
32.67
40.33
44.13
41.61
Ishot-vision-dyscalc
39.61
43.23
38.34
48.48
51.02
49.37
D
Ishot-vision
38.98
42.27
37.93
47.54
50.00
48.43
Oshot-vision-cot
51.13
51.34
51.06
53.27
54.16
53.60
Oshot-vision-calc
41.31
44.80
41.82
46.76
52.22
48.64
G
Oshot-vision-dyyscal
36,48
45.76
36.67
45.49
54.89
48.67
Oshot-vision
27.27
45.87
27.82
37.77
49.50
41.68
Ishot-text-sO.3-co
37.44
36.65
35.74
42.01
42.32
42.14
Ishot-text-s0.3
30.18
31.05
29.77
36.14
37.59
36.68
K
2.06
4.73
2.30
5.75
9.78
7.09
Trend
Dshottteet-s00.--ppa
46.08
46.75
46.14
47.07
48.13
47.43
M
Oshhot-eextss))3-ddyclll
11.38
13.71
11.63
14.30
17.62
15.41
0shoo-teetts00.-ccsw
0.50
0.50
0.50
5.45
9.52
6.83
O
Oshot-text-:033-ccot-pp
10.76
11.00
10.77
11.39
12.00
11.60
0shot-text=s003-coot-p0
20.40
20.84
20.42
21.31
22.32
21.65
Q
0shot-text-s033-cot--cs
4.52
4.75
4.54
8.09
9.73
8.66
Oshot-text-s0,3-cot
13.26
13.50
13.27
15.42
17.52
16.12
Oshot-text-s0.3-can
11.20
12.58
11.34
12.87
14.77
13.50
Oshot-text-s0.3
33.58
32.66
31.95
38.68
39.79
39.07
U
Oshot-text
19.21
23.60
19.56
22.80
26.84
24.16
Our
Oshot-text
32.32
52.20
35.72
47.85
54.93
50.59
Our
Oshot-text-vision
56.90
66.60
58.83
68.36
74.21
70.60
A
Ishot-vision-cot
2.76
8.41
3.58
35.98
42.04
36.75
Ishot-vision-call
4.55
13.45
5.19
35.04
38.20
33.78
C
Ishot-vision-dyscale
4.11
12.28
5.37
35.10
38.41
33.73
Ishot-vision
4.31
15.22
4.92
38.93
44.67
38.43
Oshot-vision-cot
4.97
9.25
5.37
30.18
27.70
27.27
Oshot-vision-calc
4.68
15.55
5.41
26.42
29.19
25.80
Oshot-vision-dyscaal
7.16
18.74
8.88
32.78
33.72
31.01
Oshot-vision
6.51
17.82
8.81
35.94
35.14
33.37
Ishot-text-sO)3-com
3.05
2.69
2.40
30.54
26.81
26.86
1shot-text-s0.3
4.24
4.93
3.22
37.50
32.79
32.66
Freq
37

---

K
Oshot-text-sO.3-11
4.01
7.60
4.01
24.88
21.46
21.45
Oshot-text-sO.33
8.36
9.29
8.38
13.77
12.68
12.77
M
Oshot-text-s0)3-dyyeee
7.15
8.20
7.22
16.55
13.84
14.39
-shoo--eext-s0.-ees
3.65
8.97
3.62
37.52
31.89
32.68
0shot-text::0)33cco--pp
3.13
3.73
3.15
10.65
8.46
8.98
Oshot-tectt=003-ccooppp
3.79
3.68
3.71
6.45
5.57
5.78
Oshot-text-sO.3-coo
2.60
4.52
2.69
19.44
17.81
17.71
Oshot-text-s003-cco
6.51
7.71
6.72
12.89
11.98
12.06
Oshot-text-sO.3-cal
8.18
9.48
8.30
14.36
13.50
13.45
Oshot-text-s0.3
3.38
7.18
3.33
37.12
33,84
33.16
U
Oshot-text
5,66
12.39
6.04
15.82
17.30
15.53
Our
Oshot-text
20.35
48.90
19.72
60.36
63.00
57.04
Our
Oshot-text-vision
13.42
46.70
17.22
57.58
73.33
62.47
A
lshot-vision-cot
3.87
12.65
4.61
33.66
45.65
37.30
-shot-vision-cale
10.01
29.92
12.26
44.34
57.93
48,86
C
Ishot-vision-dyssale
8.98
31.81
11.90
42.77
59.47
48.65
Ishot-vision
9.97
29.84
12.99
44.76
57.62
49.26
Oshot-vision-cot
4.35
12.95
5.47
31.51
39.00
33.61
Oshot-vision-calc
22.97
58.75
28.77
66.27
76.14
70.44
G
Oshot-vision-dyssaal
17.31
54.36
23.36
59,49
70.15
63.79
Oshot-vision
14.26
53.10
20.64
56.18
67.83
60.77
Ishot-text-s0.--cot
10.20
9.98
9.43
30.63
30.69
29.49
Ishot-text-s0.3
8.69
9.89
7.70
32.37
32.95
31.19
Oshot-text-sO.3--pp
7.00
7.00
7.00
14.89
15.08
14.88
Point
L
Oshot-text-sO.3-ppp
25.26
25.50
25.27
27.14
27.26
27.16
M
Oshoo-teetts0.---yyeeaa
18.89
19.66
18.94
23.35
23.80
23.46
Oshot-text-s0.3-00
11.92
13.91
11.20
40.05
38.01
37.78
-ooot-text-O0.--cot--t
9.21
9.82
8.81
26.12
24.75
24.70
P
Oshot-text-soe
10.58
10.83
10.58
11.56
11.76
11.62
9.00
9.00
9.00
11.69
11.68
11.63
R
Oshot-text-s0.3
15.82
16.50
15.86
18.51
19.07
18.68
Oshot-text-sOl3-call
21.42
23.25
21.52
25.62
26.72
25.93
T
Oshot-text-s0.3
10.21
11.34
9.96
33.19
34.47
32.60
Oshot-text
19.26
22.48
19.47
25.08
27.32
25.84
Our
Oshot-text
37.76
53.21
37.08
59.81
65.15
60.64
Our
Oshot-text-vision
28.86
60.61
34.92
56.23
69.93
61.22
Ishot-vision-cot
3.57
5.90
3.66
31.10
34.97
31.58
Ishot-vision-cal.
19.32
35.23
21.91
60.17
65.54
61.65
lshot-vision-dyscale
17.24
34.25
20.14
59.85
65.48
61.53
D
Ishot-vision
17.79
33.39
20.35
58.16
61.04
58.53
Oshot-vision-cot
8.25
12.05
8.64
34.21
36.28
33.99
F
Oshot-vision-calc
27.58
56.64
32.82
72.83
78.02
74.93
Oshot-vision--yysall
4.39
13.78
5.03
32.84
39.45
34.03
H
Oshot-vision
27.08
56.67
32.58
73.52
79.03
75.84
Ishot-text-s0,3-coo
8.70
8.90
8.48
28.66
28.08
27.47
Ishot-text-s0.3
8.61
9.95
8.37
27.64
26.60
25.94
Oshot-text-s0.3--pg
7.83
12.72
8.05
22.23
23.44
21.88
Range
L
Oshot-text-sO.3-pap
25,43
27.34
25.51
28.87
29.71
29.00
Oshot-text-s0.3-dyyoae
7.56
16.99
7.92
21.17
25.37
22.00
Oshot-text-s0.3-csv
7.83
8.24
7.49
38.79
34.59
35.52
0shoo-text-s0033-cott--
6,40
6.46
6.34
11.74
11.09
11.12
0shoo-textss033-coo-pppa
12.58
12.40
12.37
18.20
17.09
17.31
Q
0shot-text-5003-coot-cs
4.83
5.71
4.75
19.71
18.08
18.24
Oshot-text-s0.3-coo
9.85
10.45
9.94
16.89
17.87
17.13
Sshot-text-s0,3-caal
10.49
13.55
10.29
20.98
21.34
20.46
0shot-text-s0.3
7.45
8.89
7.48
25.94
25.18
24.59
U
Oshot-text
8.95
27.30
9.39
27.16
36.32
29.81
38

---

Our Our
Oshot-text
20.27
54.87
24.33
49.22 50.37
59.77
51.89
Oshot-text-vision
20.82
63.50
27.68
69.70
57.33
GPT-4o
Standard
Affiliation
Datasets
Code
Prompt
Prec.
Recall
F1
Prec.
Recall
F1
A
Ishot-vision-cot
57.50
57.50
57.50
57.50
57.50
57.50
Oshot-vision
57.50
57.50
57.50
57.50
57.50
57.50
Oshot-text
6.67
5.91
6.04
14.85
22.53
17.48
Trend
Our
Oshot-text
83.47
68.97
70.84
87.48
87.93
87.41
Our
Oshot-text-vision (w/o value)
58.13
57.82
57.90
58.23
58.21
58.22
Our
Oshot-text-vision (w/o index)
64.25
64.40
63.80
71.40
71.87
71.63
Our
Oshot-text-vision (w/o deseason)
57.50
57.50
57.50
57.50
57.50
57.50
Our
Oshot-text-vision
79.12
75.48
76.27
79.66
79.43
79.54
ishot-vision-cot
15.66
17.93
16.12
28.10
22.09
23.65
H
Oshot-vision
13.68
15.69
14.27
19.02
16.13
16.98
Oshot-text
15.01
10.54
11.20
34.05
24.71
27.40
Freq
Our
Oshot-text
50.38
27.15
29.34
72.56
60.06
62.88
Our
Oshot-text-vision (w/o value)
16.27
20.95
16.82
37.87
31.69
33.40
Our
Oshot-text-vision (w/o index)
16.80
20.87
17.06
52.02
41,44
44.39
Our
Oshot-text-vision (w/o deseason)
37.86
19.01
22.13
39.54
27.41
30.80
Our
Oshot-text-vision
45.42
36.09
35.16
65.85
51.44
55.66
A
Ishot-vision-cot
30.12
46.21
33.57
72.69
73.24
72.08
Oshot-vision
45.20
68.00
50.48
87.71
87.70
87.09
U
Oshot-text
33.49
32.11
32.31
73.21
73.64
72.87
Point
Our
Oshot-text
72.50
36.66
44.00
74.48
74.86
73.52
Our
Oshot-text-vision (w/o value)
46.67
63.10
50.96
87.29
85.85
85.87
Our
Oshot-text-vision (w/o index)
44.10
44.11
43.30
86.06
82.98
83.65
Our
Oshot-text-vision (w/o deseason)
98.87
90.33
93.57
99.96
97.76
98.54
Our
Oshot-text-vision
90.51
73.14
78.71
92.66
91.48
91.79
Ishot-vision-cot
22.65
32.35
24.52
67.43
70.52
68.34
H
Oshot-vision
41.76
61.19
45.87
84.52
86.62
85.15
Oshot-text
23.58
21.90
21.47
63.27
61.17
61.18
Range
Our
Oshot-text
72.56
66.75
67.40
76.20
77.96
76.43
Our
Oshot-text-vision (w/o value)
42.21
58.69
45,99
83.33
83.35
82.81
Our
Oshot-text-vision (w/o index)
41.77
46.07
42.70
84.21
83.73
83.44
Our
Oshot-text-vision (w/o descason)
96.39
88.93
91.67
97.75
96.20
96.72
Our
Oshot-text-vision
92.70
88.95
90.00
94.25
93.05
93.45
Qwen2.5-VL-72BB-M Instruct
Standard
Affiliation
Datasets Code
Prompt
Prec.
Recall
F1
Prec.
Recall
F1
lshot-vision-cot
54.59
55.00
54.66
54.94
55.00
54.97
-shot-vision-cale
43.78
44.79
42.79
48.13
48.69
48.37
shoo-vision-dyysale
42.98
46.53
42.82
49.49
50.76
50.03
E
Oshot-vision-cot
55.93
55.89
55.81
56.19
56.22
56.20
Oshot-vision-calc
61.30
60.87
60.69
62.07
62.06
62.06
Oshot-vision--yssall
60.75
60.74
60.43
61.56
61.62
61.58
Oshot-vision
60.19
59.95
59.79
60.64
60.64
60.64
Ishot-text-s0.3
45.52
45.51
45.52
47.48
47.99
47.69
Oshot-text-s0.3-ppie
40.50
40.50
40.50
40.76
40.84
40.79
sooo---ext-s0.--yycca
10.25
10.25
10.25
10.98
11.38
11.13
Trend
39

---

N
-ooot-text-s0.--c
16.75
16.75
16.75
19.08
20.10
19.44
Oshot-text-s0.3
18.75
18.75
18.75
19.01
19.15
19.06
S Sahot-text-sO.3-call
8.81
8.80
8.81
10.03
10.88
10.33
Oshot-text-sO..3
46.81
46.77
46.78
48.76
49.48
49.02
U
Oshot-text
56.00
56.00
56.00
56.08
56.14
56.11
Our
Oshot-text
86.06
83.24
82.00
91.96
93.96
92.69
Our
Oshot-text-vision (w/o value)
61.38
60.66
60.77
62.70
62.86
62.77
Our
Oshot-text-vision (w/o index)
66.82
68.73
66.34
81.77
84.62
82.88
Our
Oshot-text-vision (w/o deseason)
60.80
59.36
59.68
61.37
61.27
61.31
Our
Oshot-text-vision
85.61
81.49
81.09
89.95
91.19
90.20
Ishot-vision-cot
5.16
7.29
5.35
16.12
14.46
14.67
B
Ishot-vision-calc
10.98
11.84
10.00
28.72
23.05
23.95
Ishot-vision-dyscalc
10.15
11.80
9.17
31.71
26.34
26.81
E
Oshot-vision-cot
9.33
13.26
9.93
17.26
16.61
16.33
Oshot-vision-cale
14.29
15.52
14.15
31.78
24.87
26.59
G
Oshot-vision-dyscale
16.40
18.10
16.39
31.99
24.42
26.38
Oshot-vision
15.47
18.62
15.75
29.05
26.55
26.64
Ishot-text-s0.3
16.39
16.31
15,45
63.17
50.72
54.66
Oshot-text-s0.3-pet
6.75
6.75
6.75
7.98
7.36
7.52
M
Oshoo-textts00.-dyyseel
3.72
3.56
3.50
19.20
14.15
15.51
Freq
Oshot-text-s003--csy
11.16
9.81
9.98
33.34
23.73
26.37
Oshot-text-s003-coot
4.81
4.82
4.81
5.26
5.06
5.12
Oshot-text-s003-caa
5.51
4.96
4.76
19.25
13.76
15.27
Oshot-text-sO.3
19.30
18.79
18.14
64.73
52.41
56.38
U
Oshot-text
11.45
10.11
10.20
18.80
14.19
15.43
Our
Oshot-text
56.91
30.77
33.57
80.90
62.84
67.54
Our
Oshot-text-vision (w/o value)
23.87
28.34
22.97
61.32
51.76
53.90
Our
Oshot-text-vision (w/o index)
21.97
27.84
21.22
63.38
53.50
55.76
Our
Oshot-text-vision (w/o deseason)
42.81
28.58
31.05
47.43
37.21
40.30
Our
Oshot-text-vision
49.41
41.14
37.88
79.18
64.95
68.57
Ishot-vision-cot
18.47
22.57
18.98
35.89
38.85
36.88
Ishot-vision-calc
41.02
43.32
40.04
73.97
79.19
75.82
Ishot-vision-dyysale
37.43
42.19
37.49
70.08
75.13
71.84
E
Oshot-vision-cot
25.04
32.84
26.86
44.71
46,66
45.33
Oshot-vision-calc
46.25
51.14
46.36
82.45
83.61
82.77
G
Oshot-vision-dyssaal
47.30
53.39
47.98
82.86
84.52
83.41
Oshot-vision
60.09
67.56
61.62
94.93
96.19
95.34
Ishot-text-sO.3
33.19
31.96
32.18
70.90
65.03
66.95
Oshot-text-sO.3-pap
16.25
16.25
16.25
18.21
17.68
17.83
M
Oshot-text-s0)3--dyeae
12.62
12.19
12.27
26.96
23.93
24.80
Point
Dshot-text-sO.3-ccv
18.06
16.77
17.03
39.32
34.06
35.67
P
0shot-text-s0)3--com
8.50
8.50
8.50
8.85
8.70
8.75
Oshot-text-s003-ccll
6.61
6.36
6.41
18.27
15.88
16.57
T
Oshot-text-s0.3
29,66
29.23
29.16
68.38
62.84
64.63
Oshot-text
30.33
29.85
29.83
62.56
54.68
57.09
Our
Oshot-text
94.52
56.07
63.16
98.31
91.50
93.78
Our
Oshot-text-vision (w/o value)
56.83
58.55
55.57
92.67
90.89
91.23
Our
Oshot-text-vision (w/o index)
52,61
54.97
52.19
89.04
89.42
88.50
Our
Oshot-text-vision (w/o deseason)
90.61
95.63
91.74
99.36
99.53
99.36
Our
Oshot-text-vision
94.19
83.80
85,98
99.01
96.94
97.70
A
Ishot-vision-cot
30.14
31.44
29.92
51.12
$1.95
$1.00
Ishot-vision-calc
49.75
51.22
48.58
94.28
91.52
92.25
C
Ishot-vision-dyscale
49.84
51.61
48.97
94.03
90.73
91.68
Oshot-vision-cot
36.46
43.52
37.61
61.41
61.55
61.27
Oshot-vision-cale
46.03
55.76
48.02
92.50
91.52
91.57
Dshot-vision-dyscalc
46.97
58.48
49.17
91.06
91.02
90.67
40
Range

---

H
Oshot-vision
47.35
57.86
49.43
93.36
92.56
92.49
Ishot-text-s0.3
14.26
16.71
14.76
50.94
55.36
52.01
Oshot-text-sO.3-pap
9.65
9.30
9.39
22.67
20.76
21.09
Oxhho--eetts0.3-ddssall
2.35
2.30
2.31
8.47
7.66
7.84
N
Oshot-text-s0.3-cww
9.77
9.14
9.20
32.94
28.68
29.93
Oshoo--extt-3033000----
3.94
3.33
3.40
6.23
5.32
5.52
Oshot-text-sO.3-e0
0.69
0.56
0.59
3.37
2.92
3.03
Oshot-text-s0.33
11.42
13.58
11.85
46.77
51.10
47.91
U
Oshot-text
6.19
6.68
5.93
45.32
44.74
43.80
Our
Oshot-text
52.40
46.27
45.78
60.49
65.31
61.84
Our
Oshot-text-vision (w/o value)
47.70
53.44
48.04
91.94
89.46
90.06
Our
Oshot-text-vision (w/o index)
13.26
22.41
15.17
53.77
61.26
56.37
Our
Oshot-text-vision (w/o deseason)
61.49
66.94
62.43
70.08
73.02
71.26
Our
Osxtt-text-vision
59.79
67.18
61.21
69.91
73.02
71.03
41

---

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48


Reviews:
[{"review_id": "2rWK3USkue", "reviewer": "Reviewer_kXX6", "summary": "This study examines LLM usage in the context of time-series anomaly detection (TSAD). The authors explored why LLMs are prone to fail TSAD tasks and proposed improving prompting strategies based on the removal of seasonality and index-aware prompting. In addition, the authors also compared LLM-based TSAD strategies against traditional non-LLM-based approaches. They found that an LLM approach achieved the overall highest F1 score and can take advantage of context to isolate anomalies.", "questions": "The analysis in the introduction provided a detailed explanation of \"how\" LLMs are failing the TSAD tasks, but not \"why\". It is challenging to offer interpretability for the opaque black box that is an LLM, but this manuscript could benefit greatly from an additional interpretability analysis, such as delving into the LLM's attention head importance, etc. \n\nUsing the proposed prompting strategy improved the performance significantly on some models (e.g., GPT-4o) but only slightly on others (e.g., LLaMA 3). What are the implications of these inconsistencies across different models? Is there a common characteristic among the high-performing models? What LLM should a person ultimately choose when facing a TSAD task?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Quality: The study is, in general, well supported by experimental results and evidence. However, I am not convinced that it answered the question \"WHY LLMs fail at TSAD.\" Instead, the manuscript explains clearly \"HOW LLMs fail at TSAD.\" by observing the pattern that LLMs can fail at certain types of anomalies based on prompt types and can fail at identifying anomaly locations. As to why these observations happen, the manuscript does not offer a satisfying explanation. I recommend either changing the wording from \"why\" to \"how\" or digging deeper to find out the root cause of these observations.\nClarity: The manuscript is clearly written and well organized.\nSignificance: This study provides a better data preprocessing and prompting strategy to drastically improve the performance of LLM on the task of TSAD. It provides a use case of zero-shot TSAD using an LLM that has a wide range of real-world applications. However, I am concerned by the results that this prompting strategy has a varying degree of improvement across different LLMs. The manuscript does not provide an explanation for why using this on GPT-4o resulted in much higher improvement than using it on LLaMA 3. \nOriginality: This study provides a new time-series prompting strategy that outperforms established methods. It also provides a comparison with non-LLM methods in TSAD, which is lacking in current research.", "was_revised": true, "final_justification": "I appreciate the authors taking the time and effort to interpret LLM behavior and perform the additional evaluations on LLM types. My original concerns are addressed.", "rebuttal": "> **Q1) The analysis in the introduction provided a detailed explanation of \"how\" LLMs are failing the TSAD tasks, but not \"why\". It is challenging to offer interpretability for the opaque black box that is an LLM, but this manuscript could benefit greatly from an additional interpretability analysis, such as delving into the LLM's attention head importance, etc.**\n\nWe thank the reviewer kXX6 for the insightful feedback. We fully agree that a more in‑depth analysis of interpretability can better illuminate the underlying mechanisms in our study.\nDue to the limited rebuttal period and resource constraints, we were unable to conduct experiments at the same scale as those presented in the main paper. However, we share an important finding based on an analysis of attention weights to address the reviewer's concern.\n\nThe deseasonalization technique we proposed aims to mitigate the issue of subtle anomalies being obscured by dominant seasonal patterns. We hypothesize that if deseasonalization effectively removes such dominant seasonal components, LLMs will be better able to attend to subtle anomalies. Consequently, we expect the attention weights over anomalous regions to vary depending on whether deseasonalization is applied. Specifically, we use the phrase \"Anomalous pattern\" as the query and the textual time series (i.e., the sequence in the prompt) as the key. The table below presents the results of this experiment on trend and frequency anomaly datasets. As a metric, _Increased Sample Ratio_ refers to the proportion of samples in which the attention weight on the anomaly segments increased after removing seasonality. _Avg. Attention Improvement_ indicates the overall average of the increased attention weights ratio per sample compared to before.\n\n**Rebuttal-Table 1. Attention analysis results showing that deseasonalization increases LLM focus on anomalous regions.**   \n| Model                  | Increased Sample Ratio (Trend) | Avg. Attention Improvement (Trend) | Increased Sample Ratio (Freq.) | Avg. Attention Improvement (Freq.) |\n|------------------------|-------------------------------|------------------------------------|-------------------------------|------------------------------------|\n| LLaMA-3.1-8B-Instruct  | 82.35%                        | 28.91%                             | 77.51%                        | 120.85%                            |\n| Qwen2.5-3B-Instruct    | 77.64%                        | 23.51%                             | 71.98%                        | 99.75%                             |\n\nAs shown in the table, deseasonalization consistently enhances attention to anomalous regions across both Trend and Freq. anomaly datasets.\n\nFor example, LLaMA-3.1-8B-Instruct showed increased attention in 82.35% of Trend samples (28.91% avg. gain) and 77.51% of Freq. samples (120.85% avg. gain). Similarly, Qwen2.5-3B-Instruct exhibited increases in 77.64% of Trend samples (23.51% avg. gain) and 71.98% of Freq. samples (99.75% avg. gain). These results indicate that deseasonalization helps LLMs better focus on subtle anomalies by reducing dominant seasonal noise, boosting detection performance.\n\nApart from the in-depth experiments, we would like to carefully explain our view on the distinction between “why” and “how”. Prior works applying LLMs to TSAD have primarily focused on performance evaluation, often using observational analyses to characterize **how these models fail** (e.g., LLMs fail more frequently on trend and frequency anomalies than on point and range anomalies), with few explicitly exploring the underlying mechanisms. In contrast, our paper takes a step toward answering **why LLMs underperform in TSAD** by explicitly identifying two causes: (1) insufficient time‑series understanding and (2) limited localization (counting) ability. We uncover these two core limitations and propose prompt‑design strategies that directly target them. These strategies lead to measurable performance improvements, which we believe constitute a practical and meaningful contribution beyond prior work.\n\nHowever, we still appreciate the reviewer’s point that what we call “why” can, at a deeper level, still be interpreted as another form of “how”. We view this as intrinsic to scientific inquiry: as one probes deeper causal mechanisms, new questions naturally emerge. For example,  following the reviewer’s suggestion, we performed an interpretation of attention heads and qualitatively confirmed that, in the absence of our proposed deseasonalization, attention tends to be absorbed by seasonal patterns. This finding naturally leads to a deeper \"why\", prompting questions about the origin of specific attention patterns. In turn, previous explanations become another form of \"how\".\n\nUltimately, our contribution provides insights into underlying causes of failure that were not previously addressed in the literature, laying groundwork for deeper analyses suggested by the reviewer. We agree that further interpretability studies, such as more extensive analyses of attention heads and embedding dynamics, are highly valuable directions as future research topics. Once again, we are grateful for the reviewer’s perspective; this feedback will serve as important guidance for our subsequent research.\n\n\n> **Q2) Using the proposed prompting strategy improved the performance significantly on some models (e.g., GPT-4o) but only slightly on others (e.g., LLaMA3). What are the implications of these inconsistencies across different models? Is there a common characteristic among the high-performing models? What LLM should a person ultimately choose when facing a TSAD task?**\n\nAs the reviewer pointed out, despite applying the same prompting strategy, the degree of performance improvement varied across models. Our proposed method led to meaningful performance gains across all evaluated models, including GPT-4o, Qwen, Gemini, and LLaMA3. Nevertheless, even under identical settings, GPT-4o, Qwen, and Gemini exhibited substantially greater improvement than LLaMA3. \n\nWe fully agree that this discrepancy warrants further analysis. To better understand this gap, we investigated the common characteristics of the higher-performing models by analyzing the correlation between their general reasoning ability and TSAD performance. Specifically, we compared the models using MMLU (for language reasoning) and MMMU (for visual reasoning), which are representative benchmarks for multimodal LLMs.\n\n**Rebuttal-Table 2. Comparison of TSAD performance gains across LLMs, highlighting the relationship between general reasoning ability and TSAD improvement.**\n| Benchmark        | Method    | GPT-4o | Qwen2.5-VL-72B-Instruct | Gemini-1.5-Flash-002 | InternVL2-LLaMA3-76B |\n|------------------|-----------|--------|---------------------------|-----------------------|----------------------|\n| TSAD(F1)          | AnomLLM   | 42.03  | 46.65                    | 43.56                | 33.67               |\n| TSAD(F1)          | Our       | 70.04  | 67.56                    | 63.31                | 34.66               |\n| MMMU             | -         | 69.10  | 64.50                    | 56.10                | 58.20               |\n| MMLU-Pro         | -         | 74.68  | 71.59                    | 64.09                | 56.20               |\n| MMLU-Pro (STEM + Economics) |           | 72.83  | 73.36                    | 63.60                | 52.50               |\n| MMLU-Pro (Others)           |           | 76.50  | 70.61                    | 64.98                | 61.27               |\n* We used the scores from the Hugging Face MMLU-Pro leaderboard and the official MMMU leaderboard. For MMLU-Pro, we referred to the corresponding language-only models of each architecture (e.g., Qwen2.5-72B-Instruct, LLaMA3-70B-Instruct).\n\nInterestingly, within the limited scope of our study, models achieving higher scores on MMLU and MMMU benchmarks also showed greater TSAD improvements. For example, GPT-4o (MMLU 74.68, MMMU 69.10), Qwen (71.59, 64.50), and Gemini (64.09, 56.10) all saw large F1 improvements, +28.01, +20.91, and +19.75 points respectively. In contrast, Intern-VLM, with lower scores on both MMLU (56.20) and MMMU (58.20), showed only a marginal TSAD improvement of +0.99.\n\nWe then looked more closely at numerically demanding subdomains of MMLU, such as STEM (math, physics, chemistry, engineering) and Economics. These subdomains are especially relevant to TSAD, which often requires numerical pattern recognition and quantitative reasoning. In these areas, the performance gap between LLaMA3 and the other models became even more pronounced: GPT-4o (72.83), Qwen (73.36), and Gemini (63.60) significantly outperformed LLaMA3 (52.50), widening the relative gap seen in overall MMLU.\n\nLastly, we also considered the reviewer’s practical question: “Which LLM should one choose for TSAD tasks?” Since running full TSAD benchmarks for each new LLM is impractical, we suggest using numerically-oriented MMLU subcategories as a reasonable proxy for TSAD capability. Our empirical results support this idea, indicating that MMLU scores can partially predict a model’s effectiveness in TSAD, offering a practical and scalable alternative to direct TSAD benchmarking."}, {"review_id": "16DF0RRPXa", "reviewer": "Reviewer_NVeG", "summary": "This paper identifies two challenges, temporal understanding and anomaly localization, in LLM-based time series anomaly detection. Correspondingly, the authors proposes a novel method to solve these challenges. Comprehensive experimental results support the claim of this paper.", "questions": "Q1. In Fig 2, the types of different anomalies should be further explained.  \nQ2. While the motivation behind Section 2.2 is well, the experiment results could be further explained. Is this setting identical to directly query LLMs to return the coordinates given one image with highlighted anomaly region? Intuitively, LLMs should perfectly give the results. But why there is still a big margin to the perfect prediction? The authors need to give a convincing reason.  \nQ3. The claim in line 143 to 146 should be theoretically supported or experimentally supported. Or recent related findings by others should be provided.  \nQ4. The authors need to give more details of the three steps of Section 3.1. What is the role of ACF function? How can step 2 be implemented?  \nQ5. The experiment settings of Section 4.2.2 should be further explained. How does each variant be implemented?  \nQ6. The provided experiment in Section 5.2 should be further explained. What is the difference between temporal context and pattern context here? How does Fig 8 support the claim in Section 5.2?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:  \nS1. The research topic is important, and the proposed method is novel.  \nS2. The pre-experiment w.r.t. the analysis for existing challenges is well-movitated.  \nS3. The paper is well-written and easy to follow.  \n\nWeakness:   \nW1. In Fig 2, the types of different anomalies should be further explained.  \nW2. While the motivation behind Section 2.2 is well, the experiment results could be further explained.   \nW3. The claim in line 143 to 146 should be theoretically supported or experimentally supported.    \nW4. The authors need to give more details of the three steps of Section 3.1. What is the role of ACF function? How can step 2 be implemented? \n\nThe details and corresponding questions about these weaknesses can be found in Questions below.", "was_revised": true, "final_justification": "I recommend Borderline accept. My main concerns have been addressed after author rebuttal.", "rebuttal": "We appreciate the reviewer NVeG’s constructive feedback and have made our best effort to address the concerns thoroughly.\n\n> **W1, Q1) In Fig 2, the types of different anomalies should be further explained.**\n\nWhile the definitions of each anomaly type are provided in Appendix B.2, we acknowledge that including a concise explanation in Section 2.1, would improve clarity and self-containment. To address this, we briefly describe the four anomaly types as follows:\n\n* _Contextual Point_: a single timestamp shows a sharp deviation from its neighbors.\n* _Out of Range_: a contiguous segment of values deviates significantly from the expected pattern.\n* _Trend Shift_: a structural shift in the overall slope or direction of the time series.\n* _Frequency Change_: a disruption in the expected periodicity, such as altered oscillation rate.\n\nThese anomaly types, based on the AnomLLM benchmark, reflect diverse real-world patterns. Point and range anomalies show clear, sudden deviations. In contrast, trend and frequency anomalies involve subtle changes like slow drifts or irregular cycles within normal ranges. These are harder to catch with simple rules or local statistics and need models that understand temporal dynamics. This variety supports a balanced and thorough evaluation.\n\n\n> **W2,Q2) Clarification for the large gap from perfect prediction in Figure 3**.\n\nYour understanding of the experimental setting in Section 2.2 is correct. In this setup, the LLM is provided with a time-series image where the anomaly region is visually highlighted, and the task is to return the corresponding start and end coordinates. This isolates the localization task by removing the need for anomaly understanding.\n\nHowever, even with the anomaly region clearly marked, localization remains difficult. The main reason is the sparse index visibility in plots. For long time series (e.g., 1000 timesteps), it’s not feasible to label every timestamp along the x-axis without clutter. This means the LLM cannot directly \"read\" exact indices from the image. Instead, it must infer coordinates based on the relative position of the highlighted region using only a few reference ticks. This indirect reasoning is error-prone, especially for small or subtle anomalies.\n\nThis challenge explains the performance gap observed in Section 2.2 and directly motivates one of the core design choices in our method, which is index-aware prompting. By explicitly attaching each value with its index in the time-series text, we remove the need for visual position inference and turn localization into a retrieval task.\n\n\n> **W3 Q3) The claim in line 143 to 146 should be supported.** \n\nIn response to the reviewer’s thoughtful suggestion, we conducted additional experiments using LoRA[1] fine-tuning on the Qwen2.5 and LLaMA3 models to provide empirical evidence for lines 143–146. The results show that while fine-tuning enhances the models' decomposition capabilities, it significantly degrades their performance on the MMLU benchmark, which evaluates general reasoning ability.\n\n**Rebuttal-Table 3: Impact of LoRA fine-tuning on component detection task, highlighting the trade-off between task adaptation and reasoning retention.**\n|                        | Task        | Component |  Detection      |    | |    Reasoning      |        |\n|-|-|-|-|-|-|-|-|\n| Model                  | Fine-tuning | Precision                | Recall | F1   |           | MMLU_val | MMMU   |\n| LLaMA3.1-8B-Instruct   | X           |                     0.61 |   0.58 | 0.59 |           | 68.20    | -      |\n| LLaMA3.1-8B-Instruct   | LoRA        |                     0.67 |   0.67 | 0.67 |           | 58.45    | -      |\n|    **Δ (LoRA - Base)**        | -           |                     **0.06** |   **0.09** | **0.08** |           | **-9.74**    | -      |\n| Qwen2.5-VL-3B-Instruct | X           |                     0.35 |   0.59 | 0.44 |           | 65.20    | 43.11  |\n| Qwen2.5-VL-3B-Instruct | LoRA        |                     0.76 |   0.76 | 0.75 |           | 63.79    | 32.00  |\n|   **Δ (LoRA - Base)**        |      -       |                     **0.41** |   **0.17** | **0.31** |           | **-1.41**    | **-11.11** |\n\nThese findings are in line with prior studies [2, 3, 4, 5], which have reported the difficulty of extending LLMs to new domains without compromising their original capabilities. Our results thus provide empirical support for adopting external decomposition tools, allowing LLMs to focus on their reasoning over structured input. We will include these findings in the revised version of the paper.\n\n[1] LoRA: Low-Rank Adaptation of Large Language Models, ICLR'22\n\n[2] An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning, arXiv’2308\n \n[3] Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal, ACL’24\n\n[4] Revisiting Catastrophic Forgetting in Large Language Model Tuning, ACL’24\n\n[5] Language Model Alignment with Elastic Reset, NeurIPS’23\n\n\n> **W4, Q4) More details of the three steps of Section 3.1: the role of ACF function and the implementation of step 2.**\n\n#### (1) Implementation Details of Step 2   \nIn practice, this decomposition is implemented using the _statsmodels.seasonal_decompose_ function with _model='additive'_ and the estimated period $ P^\\ast $ as the seasonal window. The full implementation is provided in the supplementary code.  \n\nFormally, given a time series $  \\\\{x_t\\\\}_{t=1}^T  $ and the dominant seasonal period $  P^\\ast $ identified via the ACF (see below), we decompose the series as: $x_t = \\tau_t + s_t + r_t, $ where $ \\tau_t $ is the trend, $ s_t $ is the seasonal component, and $ r_t $ is the residual.\n\nThe trend is estimated using a center moving average over window size $ P^\\ast $:\n\n$$\n\\tau_t = \\frac{1}{P^\\ast} \\sum_{i = -\\lfloor P^\\ast/2 \\rfloor}^{\\lfloor P^\\ast/2 \\rfloor} x_{t+i}.\n$$\n\nFor the seasonality, we compute an average of the de-trended values aligned by their phase in the seasonal cycle:\n\n$$\ns_t = \\frac{1}{N_c} \\sum_{j \\in \\mathcal{I}_c} (x_j - \\tau_j), \\quad \\text{where } c = t \\bmod P^\\ast,\\; \\mathcal{I}_c = \\{ j \\mid j \\bmod P^\\ast = c \\}.\n$$\n\nThis results in a seasonal template $ \\\\{s_1, \\dots, s_{P^\\ast}\\\\} $, which we repeat to obtain $ \\\\{s_t\\\\}_{t=1}^T $.  \nThe residual is then computed as: $r_t = x_t - \\tau_t - s_t.$\n\n#### (2) Role of ACF in the Decomposition Pipeline\nThe ACF plays a critical role in estimating the seasonal period $ P^\\ast $, which directly determines the window size for trend estimation and the cycle length for seasonality computation.\n\nWe compute the sample ACF as:\n\n$$\n\\hat{\\rho}(k) = \\frac{1}{T - k} \\sum_{t=1}^{T-k} \\frac{(x_t - \\bar{x})(x_{t+k} - \\bar{x})}{\\hat{\\sigma}^2},\n$$  \nand select the dominant period as:\n$$  \nP^\\ast = \\mathop{\\mathrm{argmax}}_{k \\in [1, T/2]}{ \\hat{\\rho}(k)}.\n$$\n\nBy aligning decomposition with the dominant autocorrelated lag, the periodic structure is removed from the input. This prevents seasonal leakage into the residual component and allows LLMs to focus on irregular or subtle anomalies. We will clarify these implementation details and rationale in the revised version of Section 3.1.\n\n\n> **Q5) The details of experiment setting in Section 4.2.2.**\n\nWe apologize for not properly guiding readers to the detailed implementation settings, which are provided in the appendix rather than in the main text.\n\nSection 4.2.2 evaluates the time-series decomposition capabilities of LLMs through two tasks: component detection and component generation. Supplementary descriptions of these tasks are provided in Appendix D.4, and the full prompt templates are shown in Figure 17.\n\nIn the component detection task, we formulate the problem as a multi-label classification setup using QA-style prompts, where the model is asked whether trend and/or seasonality are present. As ground truth, we use synthetic time series constructed with known components. To evaluate the LLM’s predictions, we apply a simple statistical decomposition method and label a component as “present” if its min-max amplitude range exceeds a fixed percentage of the original series range. This provides a consistent rule-based reference against which the LLM’s classification output can be compared.\n\nIn the component generation task, LLMs are prompted to produce the trend or seasonal component as a numeric sequence. This setup is structurally similar to LLM-based forecasting prompts, where the model generates a sequence conditioned on input. For evaluation, we compare the sequence generated by the LLM or the statistical decomposition to the ground-truth component defined during synthetic data generation, using Mean Absolute Error.\n\n> **Q6) The details of experiment in Section 5.2.**\n\nIn Section 5.2, we highlight a key strength of LLMs: their ability to perform context-aware anomaly filtering. We distinguish between two types of domain-specific context: _temporal context_, which refers to time-based events such as scheduled maintenance periods, and _pattern context_, which describes known behaviors in the time-series shape, such as occasional flat segments caused by sensor resets. By leveraging these contextual cues expressed in natural language, LLMs can selectively exclude explainable anomalies and focus on unexpected ones that align with the semantic definition of novelty.\n\nFigure 8 supports this claim by qualitatively demonstrating how LLM predictions differ with and without contextual information. LLMs can suppress such explainable patterns when justified by the given context (e.g., power restrictions or system shutdowns).\n\nHowever, this result remains qualitative and relies on simulated annotations. Therefore, there is a clear need for context-annotated TSAD benchmarks to enable rigorous quantitative evaluation. We see this as a promising avenue for future work and aim to further explore the unique reasoning capabilities of LLMs in time-series analysis."}, {"review_id": "Bk5mDRjtO3", "reviewer": "Reviewer_K63Q", "summary": "This paper investigates the limitations of Large Language Models in time series anomaly detection, pinpointing two key weaknesses: poor performance in identifying anomalous trend shifts and frequency changes, and inadequate anomaly localization. To address these issues, the authors propose a solution: plug-in de-seasonalization and index-aware prompting. Experimental results validate the effectiveness of these proposed methods.", "questions": "It would be good to highlight some failure cases of the proposed approach? Especially for comparing LLM-based approach with traditional approaches like CNN.", "limitations": "Yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "This paper is commended for its clarity, strong motivation, and technical accuracy. A notable strength is its thorough empirical analysis, which involves a systematic comparison across diverse prompts and large language models. The consistency of these results significantly bolsters the paper's conclusions.\n\nA weakness of the paper is that its proposed solution, particularly for the localization issue, appears rather simple and established. It is known for the LLM community that LLMs perform poorly at localizing the location of  a sequence of numbers (,e.g., performing carrying operations in multi-digit addition). The proposed solution, index-aware prompting, also lacks novelty. However, the experimental results still shows that this simple approach provides significant gains, eliminating the need of novel (and perhaps more complicated approach). The work can still offer important insights to the community.", "was_revised": true, "final_justification": "The authors have addressed my concerns, and I'll maintain my recommendation towards accepting the paper.", "rebuttal": "We sincerely appreciate the reviewer K63Q’s thoughtful and constructive feedback. The comments have been invaluable, and we would like to carefully address the remaining concerns, particularly the simplicity of our approach and the need for deeper analysis of its limitations.\n\n\n> **W1) Proposed solution, particularly for the localization issue, appears rather simple and established.**\n\nWe agree that incorporating explicit positional indices is conceptually straightforward and builds on established ideas, such as those presented by Liu et al. [1].\n\nThat said, as the reviewer K63Q also pointed out, even this simple approach leads to significant and consistent performance improvements in the context of LLM-based TSAD. Notably, this strategy has been largely overlooked in prior work, often without a clear analysis of why LLMs struggle in this setting. Our contribution lies not in introducing a novel technique, but in identifying localization as a fundamental bottleneck and showing that a lightweight intervention can effectively address it. As demonstrated in Table 3, this results in substantial performance gains across models.\n  \nWe believe this finding highlights an important insight: even simple modifications, when carefully aligned with underlying failure modes, can lead to unexpectedly large performance improvements. We hope this encourages further investigation into the limitations of LLMs and inspires the development of both simple and more principled solutions.\n\n[1] Large language models can deliver accurate and interpretable time series anomaly detection, arXiv’2405\n\n> **Q1) It would be good to highlight some failure cases of the proposed approach.**\n\nWe appreciate the reviewer K63Q’s suggestion to highlight failure cases of our proposed method, especially in comparison with conventional approaches such as the CNN-based detection model. \nIn line with this, we have carefully analyzed our results on the TSB-AD-U benchmark and shared several failure cases that reveal the limitations of LLM-based TSAD under specific conditions:\n\n* _Decomposition challenges with non-linear patterns:_  \nOur method relies on statistical decomposition to remove seasonality, which inherently assumes linearity. However, the MSL time series in the TSB-AD-U benchmark includes anomaly patterns that exhibit non-linear and irregular structures. As a result, the decomposition step may fail to fully isolate seasonal components, leading to residual distortions and false positives that ultimately degrade performance. In contrast, semi-supervised models such as the CNN-AD and the AutoEncoder, which are trained end-to-end, often learn to accommodate such complexities implicitly. Consequently, while our method outperforms the CNN overall on the TSB-AD-U benchmark, the CNN achieves slightly better F1 score on the MSL time series specifically.\n\n* _Limitations with long sequence visualization:_  \nFor long sequences with densely packed patterns (e.g., TAO time series in TSB-AD-U), converting the input into a single image plot compresses local patterns, making them visually indistinct. This hinders the LLM’s ability to recognize anomalies from the plot. The CNN model that processes raw sequences or local windows can be more robust in such scenarios, as they are not constrained by the spatial resolution of a visual representation. While LLMs could, in principle, also process the sequence in smaller local windows and aggregate the predictions, this significantly increases computational cost—already a known drawback of LLM-based approaches—and may not be practical for real-time or resource-constrained scenarios.\n\nWe believe these failure cases are important for understanding both the strengths and limitations of LLMs. To reflect this, we include representative qualitative examples in the revised version. While LLMs provide clear benefits such as semantic reasoning and the ability to generalize to new tasks without additional training, they are not a universal solution. As the reviewer suggests, recognizing both their potential and their limitations is essential for guiding future research in this area."}, {"review_id": "U5pa5BTkgU", "reviewer": "Reviewer_7DTS", "summary": "This paper focuses on the problem of time series anomaly detection using (zero-shot) large language models. Similar to AnomLLM [1], it primarily considers four types of anomalies: Trend Shift, Frequency Change, Contextual Point, and Out-of-Range. The authors observe that large language models struggle not only with determining whether a sequence is anomalous, but also—with even greater difficulty—with localizing the anomaly once it is detected. To improve performance, the proposed approach consists of three main strategies: 1. De-seasonalizing the time series, 2. Incorporating the visual modality of the sequence as additional input, and 3. Explicitly encoding the positional index of each value within the prompt. Experiments on several datasets report performance comparable to other strong baselines.", "questions": "1.\tSee the \"Weaknesses\" section.  \n2.\tIn the discussion on anomaly localization, the authors annotate the anomaly positions directly in the images. I wonder whether this might influence the judgment of the multimodal model, especially given the question-answering context.  \n3.\tFigure 2 suggests that detecting trend anomalies is particularly challenging, yet Figure 3 (b) shows that their localization performance is actually the best. What accounts for this discrepancy?  \n4.\tAs discussed in [4], a simplified model that removes the large language model component and uses only a few lightweight network layers can still achieve strong performance. Could the authors conduct similar anomaly detection experiments to evaluate this alternative?\n\n**References:**  \n\n[1] Zhou, Zihao, and Rose Yu. \"Can LLMs Understand Time Series Anomalies?.\"  \n[2] Cao, Defu, et al. \"Tempo: Prompt-based generative pre-trained transformer for time series forecasting.\"  \n[3] Gruver, Nate, et al. \"Large language models are zero-shot time series forecasters.\"  \n[4] Tan, Mingtian, et al. \"Are language models actually useful for time series forecasting?.\"", "limitations": "N/A", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength:\n1.\tThe problem of LLMs to time series anomaly detection is a timely and rapidly growing research frontier.  \n2.\tThe paper is generally well-written and easy to follow, with clear organization and coherent argumentation.   \n3.\tThe paper presents well-crafted visualizations and conducts a thorough set of experiments. The study explores a diverse set of prompting strategies and representation techniques. It also uncovers several interesting and noteworthy findings.  \n\nWeaknesses:\n1.\tThe analysis presented on “Why do LLMs fail at TSAD?” appears to closely align with issues already examined in prior work [1], offering limited new insight. Additionally, using anomaly detection (whether it exists or not) as a proxy to evaluate the understanding capabilities of large language models is somewhat unconvincing. However, the focus on the localization challenge represents a more compelling and well-motivated contribution.  \n2.\tThe main components of the proposed method have largely been explored in previous work—for example, the trend decomposition resembles that of Tempo [2], and the visual representation is similar to AnomLLM [1]. The index-aware prompting appears to be a novel design, though it bears some resemblance to the idea in [3]. Nevertheless, I have concerns about the potential overhead it may introduce in long-sequence scenarios.  \n3.\tFrom the experimental section of the paper: “While the method performs reasonably well even in the absence of domain-specific training, its inference speed is significantly slower because of the overhead of decoding anomaly segments into textual descriptions.”", "was_revised": true, "final_justification": "After thorough evaluation of the rebuttal and discussions with authors, other reviewers, and the Area Chair (AC), I am pleased to recommend acceptance of this paper. The authors have effectively addressed the majority of the concerns raised during the initial review. Specifically, the authors explained to me the reasons for the inconsistencies in certain figures. Considering the current hot topic of large language model for anomaly detection and the paper's solid empirical study, I am personally inclined to recommend that this paper should be accepted.", "rebuttal": "> **W1) Concerns on the problem settings.**\n\nWe sincerely thank the reviewer 7DTS for  insightful and constructive feedback. We would like to clarify the motivation behind our analysis, particularly regarding the instance-level anomaly detection task.\n\n1. _Complementary to AnomLLM, with a focus on diagnosis:_    \nAnomLLM provides an excellent foundation by benchmarking the capabilities and limitations of various LLMs across different anomaly types, validating common observations (for example, spike anomalies are easy while trend anomalies are hard). However, it does not explore why certain anomaly types are more challenging for LLMs, nor does it offer insight into how these limitations might be addressed. In contrast, our work shifts the focus from identifying what fails to understanding why it fails. We believe this makes a meaningful contribution to the community.\n\n\n2. _Instance-level detection as a lower-bound diagnostic:_   \nOur instance-level evaluation is not just about simplifying the task for LLMs, but about enabling a more fine-grained analysis of failure causes. Existing evaluation of TSAD often treats anomaly detection as a single end-to-end task, making it difficult to disentangle whether failures arise from a lack of temporal understanding or from imprecise localization. To address this, we explicitly isolate the LLM's understanding ability by designing an instance-level classification task (Figure 2 and Section 2.1) that simply asks whether any anomaly exists in a given sequence, without requiring precise localization. This removes the positional burden and serves as a lower-bound probe: if a model fails here, it likely lacks a basic grasp of the underlying time-series structure. While some of our findings are consistent with those of AnomLLM, our setup makes it easier to separate understanding from localization errors. \n\n> **W2) Concerns on the proposed method.**  \n\nWhile we agree that the individual components such as trend decomposition and visual prompts have appeared in prior work, our paper does not aim to propose a novel method in terms of architectural complexity or algorithmic innovation.\nInstead, our main contribution lies in a systematic analysis of why LLMs underperform in TSAD, disentangling the issues of temporal understanding and localization.\nThis analysis not only uncovers fundamental limitations in current LLM prompting practices, but also naturally leads to our proposed solution, which yields significant gains despite its simplicity.\n\nFor the computational overhead, while adding indices increases the input context length, the main computational bottleneck typically lies in the decoding phase. As shown in our main results (Table 3), the overall runtime is not always significantly slower than the index-free prompt. \n\n\n> **W3) Concerns on computational limitation of LLM-based TSAD.**    \n\nAs noted in Section 5.1, current LLM based TSAD methods show clear limitations in inference efficiency, especially when compared to traditional lightweight models. However, given that LLMs were not originally designed for time series data, we believe it may be premature to interpret this limitation as a decisive weakness. If LLMs can demonstrate meaningful advantages in terms of accuracy or representational capacity for time series tasks, efficiency could naturally become a key target for future optimization. Rather than evaluating the approach solely based on its current limitations, we hope it can be viewed with an eye toward its potential for further development.\n\n\n> **Q2) In the discussion on anomaly localization, the authors annotate the anomaly positions directly in the images. I wonder whether this might influence the judgment of the multimodal model, especially given the question-answering context.**\n\nIt’s true that the anomaly regions were visually annotated in the images, and this was an intentional design choice. The goal of this experiment (Section 2.2, Figure 3) was to remove the need for time-series understanding by providing ground-truth anomaly locations, thereby isolating the model’s localization ability. While trend anomalies showed near-perfect improvement under this setting, the model still failed significantly on point, range, and frequency anomalies. This highlights our key finding: even when LLMs are told where the anomalies are, they often struggle to translate that into precise start and end points, suggesting that localization itself remains a core challenge for LLM-based TSAD.\n\n>  **Q3)  Figure 2 suggests that detecting trend anomalies is particularly challenging, yet Figure 3 (b) shows that their localization performance is actually the best. What accounts for this discrepancy?**  \n\nWe would like to clarify the reviewer's observation regarding Figure 2 and Figure 3(b). While the results may appear inconsistent at first glance, they reflect differences in task setup. Figure 2 evaluates anomaly detection **without any guidance**, whereas Figure 3(b) focuses on anomaly detection **with ground truth provided**. \n\nSpecifically, Figure 2 focuses on the instance-level anomaly detection task, where the model must determine whether any anomaly exists in the input sequence, without being told where or how many anomalies are present. This setup is used to reveal that there are still anomalies that LLMs fail to detect, even in problems that are simpler than TSAD, where the goal is to isolate the model’s ability to understand temporal patterns without the influence of localization.\n\nIn contrast, Figure 3(b) evaluates the LLM's localization capability under a guided setting, where the anomaly regions are explicitly highlighted (e.g., in the input image). In this case, the model is relieved from the burden of understanding and only needs to translate the visual cues into concrete coordinate predictions. As a result, we observe substantial performance improvements, particularly for trend anomalies. This can be interpreted as the LLM previously struggling to recognize trend anomalies on its own, but in Figure 3, the anomaly locations were explicitly highlighted, allowing the model to rely on that information.\n\nIn short, the results are not contradictory; instead, the ground truth provided in Figure 3 helps mitigate the challenge highlighted in Figure 2. We will update the manuscript to make this connection clearer and help readers better understand the intent behind the two experimental settings.\n\n> **Q4) As discussed in [4], a simplified model that removes the large language model component and uses only a few lightweight network layers can still achieve strong performance. Could the authors conduct similar anomaly detection experiments to evaluate this alternative?**\n\nWe sincerely thank the reviewer for the thoughtful suggestion regarding the comparison to lightweight alternatives, such as those explored in [4]. Specifically, [4] focuses on forecasting tasks, where LLMs are alignment-tuned and tasked with generating future time series values. The conclusion that pretrained language model parameters offer limited benefit in that context is interesting, but it applies primarily to supervised and fine-tuned forecasting scenarios.\n\nIn contrast, our work focuses on zero-shot anomaly detection, where no learning or task-specific tuning is involved. And we believe is a more suitable paradigm for real-world TSAD applications due to the scarcity of labeled anomalies. Our goal is not just to replicate the performance of conventional models, but to ultimately leverage the reasoning capability of LLMs, particularly their ability to interpret domain priors and to enable context-aware filtering, as preliminarily explored in Section 5.2. In this respect, simply replacing LLMs with lightweight transformers would overlook the fundamental motivation of our approach and sacrifice their essential reasoning abilities.\n\nHowever, we completely agree that comparing against simpler and more efficient methods is critical. In Section 5.1 and Figure 7, we already include a comparison against a wide range of baselines, many of which are significantly lighter than the transformer models explored in [4], including non-parametric methods (e.g., SR, IForest, MatrixProfile) and shallow neural models (e.g., CNN, AutoEncoder, USAD). While our method achieves stronger F1 performance, we also acknowledge and highlight in the paper that these lighter models remain more practical in latency-constrained environments."}]

Author rebuttal:

Meta-review: After the final round of reviews, the paper received one borderline accept recommendation and three accept recommendations. The reviewers highlighted the relevance and timeliness of the topic, particularly the application of LLM to time series anomaly detection. They also recognized the paper's technical contributions as a strength. The rebuttal was viewed positively and contributed to an improved overall assessment, as it addressed several of the reviewers’ concerns and provided clarification on key points related to the methodology and experimental results.
Decision: Accept (poster)