When Foundation Models are One-Liners: Limitations and Future Directions for Time Series Anomaly Detection
Authors: Xiaokun Zhu
Louis Carpentier
Mathias Verbeke
Venue: ICLR 2026 Poster (2026)
Keywords: Time Series
Foundation Model
Anomaly Detection
Primary area: foundation or frontier models, including LLMs
TL;DR: We show that the current methodologies of applying time-series foundation models to time-series anomaly detection are flawed, and suggest alternative directions to make foundation models effective.

Abstract: Recent efforts have extended the foundation model paradigm from natural language to time series, raising expectations that pre-trained time-series foundation models generalize well across downstream tasks. In this work, we focus on time-series anomaly detection, in which time-series foundation models detect anomalies based on the reconstruction or forecasting error. Specifically, we critically examine the performance of five popular families of time-series foundation models: MOMENT, Chronos, TimesFM, Time-MoE, and TSPulse. We find that for each model family using varying model sizes and context window lengths, anomaly detection performance does not significantly differ to simple one-liner baselines: moving-window variance and squared-difference. These findings suggest that the key assumptions underlying reconstruction-based and forecasting-based methodologies for time-series anomaly detection are not satisfied for time-series foundation models: anomalies are not consistently harder to reconstruct or forecast. The results suggest that current approaches for leveraging foundation models in anomaly detection are insufficient. Building upon our insights, we propose alternative directions to effectively detect anomalies using foundation models, thereby unlocking their full potential for time-series anomaly detection.

Published as a conference paper at ICLR 2026
WHEN FOUNDATION MODELS ARE ONE-LINERS: LIMITATIONS AND FUTURE DIRECTIONS FOR TIME SERIES ANOMALY DETECTION
Xiaokun Zhu, Louis Carpentier's & Mathias Verbeke KU Leuven, Department of Computer Science. Belgium
Leuven.AI KU Leuven Institute for AI, Belgium
Flanders Make @ KU Leuven {xiaokun. zhu, louis wwwpent c wwthiiiseeaaaatteeeeoeo.oeeeeeee eerbbee))kuleuuerr
ABSTRACT
Recent efforts have extended the foundation model paradigm from natural lan- guage to time series, raising expectations that pre-trained time-series foundation models generalize well across downstream tasks. In this work, we focus on time- series anomaly detection, in which time-series foundation models detect anoma- lies based on the reconstruction or forecasting error. Specifically, we critically ex- amine the performance of five popular families of time-series foundation models: MOMENT, Chronos, TimesFM, Time-MoE, and TSPulse. We find that for each model family using varying model sizes and context window lengths, anomaly detection performance does not significantly differ to simple one-liner baselines: moving-window window variance and gquared-ddfferente key assumptions underlying reconstruction- -based and forecasting- bbsed method- ologies for time-series anomaly detection are not satisfied for time-series foun- dation models: anomalies are not consistently harder to reconstruct or forecast. The results suggest that current approaches for leveraging foundation models anomaly detection are insufficient. Building upon insights, we propose alter- native directions to effectively detect anomalies using foundation models, thereby unlocking their full potential for time-series anomaly detection.
These findings suggest that the
INTRODUCTION
A time series is sequence of data points that quantify any process evolving time. It is ubiq- uitous concept, since time is an intrinsic dimension of the world in which we all live. Many practical tasks revolve around time series, e.g., forecasting (Lim & Zohren 2021), classification (Middlehurst et al. 2024), and anomaly detection (Schmidl et al. 20022). Time series have much in common with natural language because both spoken and written language are sequences of words and sentences. Moreover, tasks involving natural language often parallel those in time series analysis: question an- swering and dialogue systems (Gao al. 2019) require predicting the next words for a given prompt (forecasting): spam detection and sentiment analysis (Minaee et al. (021) involve assigning text to predefined categories (classification); and grammatical error detection (Bryant et al., (023) aims to identify tokens that deviate from expected linguistic patterns (anomaly detection).
In recent years, a new paradigm has gained popularity in natural language processing: pre-training huge language models -ftte called foundation models or large language models (LLMs) on mas- sive text corpora on single task, typically next-token prediction or sequence reconstruction (Zhao) et al.) 2025). After pre-training, the models do not only excel at the training task. but also demon- strate emerging abilities on other downstream tasks (Wei et al. 2022), even outperforming traditional methods designed specifically for those tasks (Devlin et al.) 2019 Brown al. 2020).
Given the commonalities between time series and natural language, researchers have recently begun applying the foundation model methodology time series (Ansari al. 2024a Woo et al. 2024 Goswami et al. 2024 Shi et al. 2024). Mirroring the approach in natural language processing,
"Corresoonding author

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this involves pre-training time-series foundation models (TSFMs) on large-scale time series datasets using a single objective - typically forecasting or reconstruction with the expectation that the resulting models can be adapted to variety of downstream tasks and surpass task-specific methods.
One of those tasks is time-series anomaly detection (TSAD), the automatic detection of anomalous events in time series, which plays critical role in many high-stakes applications such fraud de- tection (Ferdousi & Maeda (006). fault diagnosis (Yan et al. (024), and health monitoring (Pereira & Silveira 2019), where timely identification of rare but significant deviations can prevent costly failures or save lives. Researchers have hypothesized the effectiveness of TSFMs in TSAD (Ansari al. 2024a). Although preliminary experiments seem to confirm this (Goswami al., 2024). thor- ough evaluations of these models are still lacking. A recent benchmarking study includes a more extensive evaluation of TSFMs and claims that TSFMs "show great promise", demonstrating "su- perior zero-shot capabiliries when compared to most existing statistical and neural network-based methods" (Liu & Paparrizos, 2024). However, we argue that this study insufficient for answering the following question: "are current approaches to apply TSFMs to TSAD effective?" On the one hand, the study heavily relies on aggregate metrics which overlook counterintuitive details in the results that lead to surprising findings,
will demonstrate. On the other hand, [iu & Paparrizos (2024) analyses for each family of TSFMs with only a single model size and short context window lengths. As such, the study does not explore the sensitivity of the model size and context length, while these are two crucial factors for LLMs (Kaplan et al." 2020 Liu et al. 2023
To address these limitations, we perform thorough validation of four popular families of TSFMs on the task of TSA:: MOMENT (Goswami et al. e (2024), Chronos (Ansari et al. (2024)), TimesFM (Das) al.] 2024), and Time-MoE (Shi al.) 2024). We employ two mainstream methodologies: forecasting-based (Chronos, TimesFM and Time-MoE) and reconstruction--bsed (MOMENT), which derive anomaly scores from the model's forecasting or reconstruction errors (Schmidl al., 2022). Additionally, we examine TSPulse (Ekambaram et al. 2025), which employs both method- ologies and further incorporates frequency-domain inputs. For each model family except TSPulse, we include two variants of different sizes. Each variant is evaluated using at least three different context window lengths (64, 256 and 512). Our main contributions are summarized as follows:
We show that the key assumption of reconstruction--asee and forecasting-bast anomaly detection anomalies are harder to reconstruct/forccast does not hold for TSFMs: un- der such error-based methodologies, their performance for anomaly detection is either similar not significantly better than one-liner baselines: moving- window variance (reconstruction-bact based TSFMs) and squared-difference (forecasting- results are irrespective of the model family, the model size, the context window length, or performance.
TSFMs). These
the model's reconstruction/forecasiii
We suggest alternative directions to unlock TSFMs' potential for TSAD, such as exploiting long-term and short-term forecasting horizons, detecting anomalies in hidden representa- tions, and performing end-to-end fine-tuning using labeled anomalies.
We highlight the significance of visualizing both data and algorithm outputs for individual time series, which enabled our conclusions. This point has been previously articulated with eloquence (Wu & Keogh 2023), yet it remains underappreciated within the community.
PRELIMINARIES AND RESEARCH QUESTION
A univariate time-series T (21,..., xnn is ordered sequence of E N values 2, R A
subsequence Tz,- = (I,, Tst1,...,, of with S 8 < e S n of length e e-s+1 is a contiguous segment of T starting at index and ending index While many time-series analysis tasks exist, in this work we focus on time-series anomaly detection.
1 We focus on univariate time series because the majority of TSFMs only offer native support for univariate inputs (Ansari et al. [2024a] Goswami et al. (2024 Shi et al. 2024 Das et al. [024]: thus, univariate evaluation is sufficient to assess their anomaly detection capabilities. Only recent exceptions, such as Chronos-2 (Ansari et al.] 2025) and Toto 1.0 (Cohen al.] 2025), capture inter-channel dependencies. Preliminary qualitative results for those two models also align with main findings and detailed in Appendix M
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Titw-1
De Nommallze
Normalize
Ritw
Stow (Rie Feom]?
Spets forie E [1.w)
Normalize
Normalize
Si 13/-1 fort
Begusnociin losedy
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S/4tw/2
(SE(e Plmm-)))
Sg-i41 5 for
Foreastt rROFEITTation
1S/M andhtecttre
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Figure I: Application TSFMs on TSAD. Given time series subsequence Tr,++w-1+ forecasting- based TSFM predicts the next observation 2μ+w and computes the anomaly score as the squared error with Ti+w-- reconstruction- based TSFM produces reconstruction T2,+--1 the complete subsequence T. and computes the anomaly score as the mean squared error across all obser- vations in the subsequence.
Definition (Time-series Anomaly Detection (TSAD)) Given a time-series T of length N. fime-series anomaly detection is the task of predicting anomaly scores 8; E TR+ for each I; E T. For two observations Te, J. E T. we say that æę is more anomalous than 2 if s4 > Sj.
Recently, numerous TSFMs have been proposed and some of them are reported to achieve state-of- the-art performance in TSAD (Goswami et al. 2024). While TSFMs share many similarities, key design choice is the pre-training task. Most TSFMs are trained for forecasting, while others focus reconstructing time series subsequences. To study the impact of this design choice TSAD, categorize TSFMs into forecasting- based and reconstruction-bit based models.
Definition 2 (Forecasting- based Time-Series Foundation Model) A prrecasting-baeed TSFM (forccast
TR." 10 } R deep neural network pre-trained predict the next value following context window of length w using massive set of time series.
Definition 3 (Reconstruction-basee Time-Series Foundation Model) A coonsstruction-baeed TSFM (peconstruct) R + R a deep neural network pre-trained to reconstruct time series sequences of length using massive of time series.
Forecasting- -based TSFMs are commonly applied to TSAD under the assumption that anomalous ob- servations are inherently more difficult to predict. Given context window, the TSFM forecasts the next observation. the predicted value deviates substantially from the actual observation, the point considered anomalous. As consequence, the forecasting error serves as anomaly score. In contrast, reconstruction -ased TSFMs assume that anomalous sequences are harder to reconstruct. They therefore detects anomalies by identifying the subsequences that deviate notably from their reconstruction, with the reconstruction error providing the anomaly score. Figure iillustrate these approaches in greater detail. In this work, analyze the effectiveness of these approaches for TSAD and answer the following research question:
Research question Are current approaches apply time-series foundaatiooo models to time-series anomaly detection effective?
3 BACKGROUND
3.1 DESIGN CHOICES FOR TSEMS
Due to the similarities between time series and natural languages, many ideas of LLMs have been borrowed to construct TSFMs. Nevertheless, several adaptations the architectures necessary
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to handle the time series characteristics. One of the main design choices is the learning tasks, as highlighted in Definitions and 3] but there are also multiple other important choices such as the normalization, tokenization, embedding and architecture. Table lists summary of the made choices for each model family included in this study, and more details can be found in Appendix [B] In particular, unlike the other four model families, TSPulse is tiny (1M) non-Transformer model incorporating multiple heads: a reconstruction head in the time domain (Headrmm)), reconstruction head in the frequency domain (Headæ) and a forecasting head in the time domain (Headduure). For TSAD, these heads are combined via ensembling and triangulation, where the latter is a per-dataset model selection procedure based on the heads' performance on a tuning set (Ekambaram et al.] 2025
Table I: Technical details of TSFMs
Chronos-Bolt
Time-MoE
TimesFM
MOMENT
TSPulse
Normalization Tokenization
Z-scaling Patching Residual block
Z-scaling Point value
Z-scaling
Z-scaling
RevIN
Patching
Patching
Patching
Embedding
SwiGLU
Residual block
Linear projection
Linear projection
Architecture Pretrained on
Encoder-decodeer Forecasting
Devoder-only
Decuder-only
Encoder-only
TSMixer
Forecasting
Forecasting
Reconstruction
Foree. Recon.
3.2
RELATED WORK
To our knowledge, the only extensive evaluation of TSFMs for TSAD is a benchmarking study pub- lished by Liu & Paparrizos 2024). They evaluated 32 univariate TSAD methods on the TSB-AD-U benchmark, which is introduced in the same paper to address common flaws identified in existing TSAD datasets (Wu & Keogh 2023). Among the evaluated methods were five TSFMs: OFA (Zhou et al. 2023). Lag-Ilama (Rasul et al.] (024)) Chronos (Ansari et al.) 2024a), TimesFM (Das et al., 2024). and MOMENT (Goswami et al. (024). The evaluation conditions for these models varied: OFA was not pretrained on time-series data and requires explicit fine-tuning; MOMENT was eval- uated both zero-shot and fine-tuned settings; and the other three TSFMs were assessed only in a zero-shot setting. For fine-tur mhe-tunnng, an initial segment of each time series used as training data, which consists of only normal data, to improve either the reconstruction or forecasting performance of the TSFM. According to the results, MOMENT (Goswami al. 2024 ranks among the top for univariate TSAD, with a negligible performance difference between its fine-tuned and zero-shot evaluations. The three forecasting-bet to "excel at detecting point- based anomalies". Based on these results, the authors conclude that "the performance of time-series foundation models shows great promise" (Liu & Paparrizos, 2024
TSFMs (Lag-Llama, Chronos and TimesFM) were found
4 EXPERIMENTAL SETUP
Our experiments cover five families of TSFMs: MOMENT (Goswami al., 2024). Chronos Ansari et al. 2024a b). TimesFM (Das et al., 2024), Time-MoE (Shi et al. 2024), and TSPulse (Fkambaram al. 2025). This selection includes both forecasting baseed based and reconstruction-bast
models and spans diverse model architectures. For each model family except TSPulse, we evaluate two instances of different sizes and use least three different context window lengths analyze the sensitivity of these parameters to the TSFMs' performance for TSAD. For TSPulse, we directly compare its results reported in Ekambaram et al. (2025) with our baselines under comparable conditions. Table 2 lists the details. We denote the experiment settings using the notation 'model family-model size-context window length', such as "OMMENT-bse-664" We compare the TSFMs against two simple baselines models, we
to evaluate their effectiveness for TSAD. For reconstruction--asse variance, as we found that model performance correlates positively with variance in anomalous regions (see Section 5.1 For forecasting-bast based models, inspired by the one-liners of Wu & Keogh which naively uses the mean of the neighboring observations
mooinn--wnndo
(003), we use as forecast, instead of a complex model.
Definition 4 (Moving-window variance) Given a time series T and subsequence length w, base- line Var-w computes anomaly score 8t+[w/2] = (TrianncTTT..ww-....
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Table 2: Experiment coverage
Liu & Paparrizos 2024
Ours
Model
Size
Window
Size
Window
MOMENT
base 15-base
base, large bolt-small, bolt-base
64, 256, 512! 64, 256, 512 64, 256, 512
Chronos
TimesFM
(200M)
1.0 (200M), 2.0 (500M) base, large IM
Time-MoE
covered not covered
64, 256, 512
TSPulse
512
For MOMENT-large, the window length 1024 was also covered.
Definition 5 (Squared-differenee Given lime series T and subsequence length w, baseline Last- w computes anomaly score 8i+ = (mean Crrean(Te,stw-ae (T,s+w-1) - I.tw)². Baseline Centered-w computes
anomaly score 8t+Lw/2. (mean (incan(T,,Ha w #(+[w/2] Fi+ (w/2))))
For the datasets, we followed Liu & Paparrizos (024)) by using their TSB-AD benchmark because it is the latest curated benchmark emphasizing labeling quality. More specifically, we used the evaluation set of TSB-AD-U, which contains 350 univariate time series curated from 23 univariate dataset:" [Liu & Paparrizos, 2024, Section B. B.I.1), evaluate the models' zero-shot anomaly detec- tion capabilities for univariate TSAD. We use the ID instead of the full name to denote individual time series in TSB-AD-U. For example, 'TSB-AD-U 811' denotes the time series with full name "811_Exatee Exxathlon_d_22. 2_ccciFa _r_
For the metric, we followed Liu & Paparrizos 2024) by using VUS-PR because we believe it pro- vides sufficient information for drawing our conclusions. As will become clear in our experimental results, while an evaluation metric can often serve as shortcut for uncovering insights, all of our key findings can be derived by examining intermediate results, such as anomaly scores, or by conducting representative case studies, without relying on any specific metric.
5 EXPERIMENTAL RESULTS
We discuss our findings for reconstruction--asee models (MOMENT) in Section 5.1 for forecasting- based models (Chronos, TimesFM, Time-MoE) in Section 5.2 for TSPulse in Sec- tion [5.3] and for Ton- anomaly detectors in Section 5.4 We only show representative perimental results without loss of generality; see Appendix
for complete results.
5.1 RECONSTRUCTION- MODELS
MOMENT- is almost equivalent to moving-window variance. Liu & Paparrizos (2024) shows that MOMENT-hane using a window size of 64 achieved remarkably high VUS-PR 0.81 on the Exathlon dataset, making it rank among the nop-performing anomaly detectors for this dataset. However, the mean anomaly length in Exathlon is 1577.3, which almost 25 times larger than the context window length. We found that MOMENT-base performed well on time series in which the anomalous regions have higher variance than the normal regions when visualizing the Exathlon time series along with the predicted anomaly scores (Figure 2a). In contrast, MOMENT fails to detect anomalies which have a small variance (Figure Da). This led us to suspect that MOMENT base in effect detecting the variance in its context window. To verify this speculation, compare MOMENT-base with the baseline moving-winet window variance (Definition 4 Our speculation was consolidated by individual case study (Figure 2 and D. one-to-one comparison (Figure 3). and statistical analysis (Table 3 and Appendix D. The root cause of this similarity is presented next.
Z-normalization biases anomaly scores upward in proportion to moving-window variance. MOMENT uses z-normalization for the normalization and de-normalization steps in the pipeline shown in Figure|] As result, the anomaly scores can be decomposed as the product of the vari- ance of the window (o?) and the reconstruction MSEs of the normalized window (7 (Tnorm complete derivation is provided in Appendix D
(,++w11). The
Twelve of the 23 univariate datasets derived from multivariate datasets.
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Recanstruction basie TSFM vs. Baseline
Reconstruction based TSFM vs. Baseline Vi Wiln Maataaa
Mag.aaalhhiiiiiioooooooooouuuuuu Forecasting based TSFM vs. Baseline
Forecasting-based vs. Baseline
(a) TSB-AD-U 811 (Exathlon)
(b) TSB-AD-U 309 (UCR)
Figure 2: TSFMs and baselines give similar anomaly scores. See Appendix C
for more examples.
1.0
Hermal momaliia
yort you(25)
0.8
vUS 7R 00w 5125
0.6
0.6
Farnert
0.7
0.0
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<35 635
1.0 175 200
(NOMENT 512)
900 025 023 033 380
150 175
(a) MOMENT-base-64
(b) MOMENT-base-512
Figure 3: One-to-one comparison shows that MOMENT-base and booving-windowe variance achieve similar VUS-PR scores across the 350 time series in the TSB-AD-U evaluation set (left). The histograms (right) of the reconstruction error, MSE (T.i+=-1 (reoonstruut) ((ittw-))) indicate that this similarity stems from the model's consistently poor reconstruction performance, regardless of whether the context window contains anomaly. See Figure 10 for complete results.
8it [wr/2] = of MSE
[mmor w
F (reconstruct)
(T
(1)
As shown in Figure MOMENT-base reconstructs all normalized windows with consistently high MSE near 1.1, contradicting the fundamental assumption that anomalies are harder to recon- struct. Consequently, the second factor in Equation D exhibits minimal variation, explaining why MOMENT-base performs similarly to moving-window variance for TSAD, An ablation study on the normalization method further consolidates our observation: switching to Min-Max or global z- normalization (normalizing the time series as a whole rather than the individual windows) results in a lower correlation with the baseline (Figure
Neither larger model size nor larger context window length leads to consistent improvement. Figure44 shows that while MOMENT-large yields a more widespread MSE distribution and substan- tially better reconstructs subsequences than MOMENT-base, the MSE distributions for anomalous and normal windows remain entirely overlapping El This again contradicts the core assumption that anomalies harder reconstruct. As result, although deviating more from the noving-window variance baseline, MOMENT-arge still shows no consistent advantage over the latter (see Table and Appendix | for statistical analysis).
5.2 FORECASTING-BASE MODELS
No forecasting-base based TSFM significantly outperforms the squared-differrnce baseline on the task of TSAD. As illustrated figure 5 all models achieve similar performance and fail to sig- nificantly surpass the squared-differene baseline (Definition 5), regardless of variations in their
-Constant-value windows are not counted as they are trivially reconstructed. We demonstrate in Appendix G that this is result of data contamination.

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1.0
1.0
V85 P% Nat 360448
0.
0.6
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30
0.0
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0.0 02 0.6 0.6
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1.0
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3.00
00 0.2
VUS-PP (NDNEN-ange-122)
0.8
0000 025 035
0.19
3.9
(a) MOMENT-large-512
(b) MOMENT-laree-1000
Figure 4: One-to-one comparison shows that MOMENT- does not consistently outperform moving-window variance across the 350 time series in the TSB-AD-U evaluation set (left). The his-
tograms (right) the reconstruction error, MSE ( i. (rccnsstmmtt) (2,.ftw-1)) suggest that reconstruction enrors are not consistently higher for context windows containing anomalies. See Figure for complete results.
forecasting performance The statistical analysis shown partly Table and fully in Appendix J further consolidated this observation. In fact, the VUS-PR scores of all model variants and baselines are also highly correlated with minimum correlation of 0.9 (Figure (3). In addition, we observed that in some time series, the higher VUS-PR scores achieved by TSFMs stem from issues with the ground truth labeling and the metric's 's tendency to amplify minor differences. Removing these instances further reduces the performance gap between the baselines and the TSFMs (Figure 16). These secondary findings are detailed in Appendix H
Single-step forecasting horizon is the primary limiting factor. Forecasting based based TSFMs excel at detecting point anomalies but fail to detect sequence anomalies (Liu & Paparrizos) 2024), because the anomaly score computed using a single forecast for given context window. For sequence anomalies. the context window overlaps with the anomaly as it slides over the time series, providing the TSFM recent anomalous observations, while the task to predict just the next one observation. As result, even anomalous values are usually not "unexpected" with regards to the context win- dow, allowing the TSFM to forecast them accurately (see Figure (6) The anomaly therefore not necessarily harder to predict, contradicting the core assumption of forecasting based approaches for TSAD. In contrast, when the TSFM forecasts multiple steps ahead from the same context window, the predictions no longer rely anomalous inputs. The sequence anomaly is then poorly predicted, as shown also in Figure 6 and the TSFM correctly detects it as amomalous. An ablation study on the normalization method offers additional evidence for observation: the high correlation with the baseline persists when switching to Min-Max, global z-normalization, or mean-scaling (Figure 17 In Section 6.1 we further explore how to exploit long-horizon forecasting.
Chrones Bolt base 512
0.89
1.0
yea(659)
Time-MoElarge-512
0.74
0.9
vus (Lazt
Last-1
1.00
0.4
Last-3
1.30
0.2
Dentered-S
0.71
0.25
1.25
Aggregate Scaled
0.0
0.2
0.4
0.6
0.8
VUS PR (T.0eFFM-2.05512
(a) TSAD (left; solid=median, dashcd=mean) versus forecasting (right).
One-to-one plot.
Figure 5: Boxplots in (a) show that all TSFM variants achieve similar performance on the TSB-AD- evaluation set, despite differences in their forecasting performance in the same dataset. Baselines (Last-3 and Centered-5) achieve comparable performance. See Figure 12 for complete results.
MASSS are calculated for each series against the Last-1 haseline and aggregated using the geometric mean. For fair comparison across different context window lengths, evaluation begins the 51.3th data point.
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Table High correlation (with extremely low p-values) and small effect size between the VUS-PR scores of TSFMs and their corresponding baselines (Moving-window variance for Reconstruction- based TSFMs and Last-3 for Forecasting-bant -ased TSFMs) over the TSB-AD-U evaluation set. See Appendix for full results.
mean (TSFM)
mean (baseline)
correlation
p-value
Cohen's
MOMENT-base-512 MOMENT-lage--12
0.311
0.313
0.991
7.91E-308
-0.047
0.313
0.313
0.857
1.38E- 102
0.003
Chronos-Bolle Bolnos--ottt
0.297
0.284
0.922
1.38e-150
0.102
Time-MoE-lagge512
0.303
0.284
0.923
8.18c-147
0.146
T'mes-PM-220-522
0.302
0.284
0.923
7.82E-147
0.142
Forecasting Horizon
Forecasting Horzoo
M Wa
Forecasting Horizon 64
Forecasting Horizon 64
M N
(a) TSB-AD-U 371 (UCR)
(b) TSB-AD-U (YAHOO)
Figure 6: Forecasts from TimesFMM-1.0511 of an anomaly at different forecasting horizons. Errors in anomalous regions are low for a short horizon (1), whereas they are high for a long horizon (64).
5.3 TSPULSE
Zero-shot TSPulse does not outperform moving-window variance and squared-difference The reconstruction and forecasting heads (Headrime and Headfuure) of TSPulse perform on par with the corresponding baselines. Moreover, the performance of triangulating or ensembling the heads is similar triangulating or ensembling the two baselines, as shown in Table 4 with TSPulse results taken directly from Ekambaram et al. (2025) Table (a)). This holds true even though TSPulse bene- fits from an additional reconstruction head in the frequency domain (Headm). These observations are not surprising because TSPulse still relies on the conventional reconstruction- and forecasting-bases TSAD methodologies (Ekambaram et al. 2025 Section A.8.2), which, as already indicated by our previous experimental results, reduce the potential of all TSFMs to the level of simple one-liners.
Table 4: Mean VUS-PR of TSPulse (zero-shot) vs. baselines the TSB-AD-U evaluation set
Reconstruction-bassd
Forecasting-
Triangulation
Ensemble
Var-96*
0.42
Last-3
0.28
0.46
0.43
Var-96*
0.42
Centered-5
0.47
0.44
Headume
0.42
Head
0.48
0.44
calculate reconstruction MSEs on only the last 96 data points of the 512-point context window. Therefore, matching window size of 96.
In their evaluation, Ekambaram et al. 2022))
5.4 NON-TSFM ANOMALY DETECTORS
We use the VUS-PR scores obtained in Liu & Paparrizos 2024) for non-TSI non-TSFM anomaly detectors for additional comparison with the baselines. Unlike the TSFMs, the correlation with the baselines low for the vast majority of non-TSFM anomaly detectors, as shown partly in Table and fully in A K Notably, the few exceptions showing relatively high correlation moving- window variance (Var), e.g., TimesNet (Wu et al. 2023). also employ z-normalization.
FUTURE DIRECTIONS TO MAKE TSFMS EFFECTIVE FOR TSAD
While our evaluation results are unfavorable to the current application of TSFMs for TSAD, we see alternative directions for improvement. We outline these possibilities in this section.
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Table 5: Correlation of VUS-PR scores over the TSB-AD-U evaluation set between non-TSFM TSAD methods and the baselines. See Appendix K for complete results.
correlation with Var
correlation with Last-3
Sub-IForest (Liu al.
0.301
0.279
Sub-LOOF [Brcuni 2000)
-0.088
0.140
MatrixProlile (Yen
2016
-0.034
0.242
KShapeAD Paparrzzs & Gravano
2017
-0.107
0.092
SAND (Bonio el al [201
-0.063
0.097
Series2Graph (Bomo) & Palpanas
2020
0.464
0.338
K.MeansAD Yauri ct atal. [0001 2001
-0.040
0.185
TimesNet (W 2023
0.805
0.724
6.1 COMBINE SHORT AND LONG FORECASTING HORIZONS
In Section 5.2] we identified the single-step forecasting horizon as the primary cause of the poor performance of forecasting-busee TSFMs in detecting sequence anomalies. A natural extension yet largely unexplored in the TSAD literature is to employ TSFM that, for a context window Th-wa-1 and forecasting horizon h, predicts Tr.4h-1 and computes the anomaly score of 2t as the mean squared eπror between T and T As shown in both individual case studies (Table o and aggregate results 7).. increasing h enhances the detection longer anomalies, but simultaneously reduces sensitivity shorter anomalies. This trade-off implies that single fixed horizon h suffices to capture anomalies in various lengths. To address this limitation, we propose ensemble approach that aggregates anomaly scores across multiple horizons using MAX-operation. This ensures that observations receive a large anomaly score when they appear anomalous under either short horizon or long horizon. Figure [8a] illustrates the effect: with h = 1, TimesFM-1.04 1.0-122 detects the point anomaly but misses the sequence anomaly, whereas with 64 the reverse occurs. By ensembling both horizons, TimesFM-1.0-511 accurately detects both anomalies.
Table 6: VUS-PR of TimeeFM--1.0051 on TSB-AD-U 371 with sequence anomaly of length 170 and TSB-AD-U 603 with point anomaly for increasing forecasting horizons h.
ID
Anomaly h
h
2
h 4
h
8
16
32
h 64
h 128
371
Sequence Point
0.04
0.21
0.27
0.36
0.46
0.65
0.68
1.0
0.8
0.9
0.94
0.9
0.47
0,27
0.02
0.9
anomaly longth
anomaly
0.6
0.7
meen WUS-BR
0.6
-----
65-258
257-
05-258
0.4
0.2
0.1
100
200
300
400
500
100
200
300
400
500
Torecasting horizon
forecasting horbzon
(a) TimesFM-1.0-512
(b) Chronos -oaooss-oot--aae Bolt-Base-Stttt---- base-512
Figure 7: Mean VUS-PR scores with various forecasting horizons, evaluated on time series con- taining anomalies of different lengths within the TSB-AD-U evaluation set. See Appendix L for complete results.
6.2 DETECT ANOMALIES IN THE HIDDEN REPRESENTATIONS
The hidden representations of TSEMs capture meaningful time series characteristics such as trend, amplitude, frequency, and phase (Goswami al. 2024). These representations be directly

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(a) TSU-AD-U 558 (Yahoo) with sequence and point anomaly. TimesFM with h 1 or h 64 fails to detect both anomalies, while their ensemble succeeds.
(b) TSB-AD-U 474 (UCR). A k-NN model using the learned representation of MOMENT significantly out- performs reconstruction- MOMENT.
Figure 8: Qualitative examples of our suggestions enhance the performance of TSFMs on TSAD.
leveraged for anomaly detection, as illustrated in Figure 8b We use MOMENT because provides direct access to hidden representations, employing the base model with a window size of 64 as Liu Paparrizos (2024). When anomaly scores are based on reconstruction error, MOMENT achieves VUS-PR of only 0.003 and ranks 31st out of 32 detectors. In contrast, applying KNN to the hidden representations effectively detects the anomaly, achieving a significantly higher VUS-PR of 0.250 and improving the rank to 5th in the benchmark of Liu & Paparrizos/ (2024). Although other detectors (e.g., KMeansAD) achieve even higher performance, this example highlights the potential of TSFM-drrreed representations enhance anomaly detection.
6.3 FINE-TUNE TSFMS FOR TSAD USING LABELED ANOMALIES
Foundation models are trained with forecasting or reconstruction objectives (Definitions 2 and 3 An intrinsic problem with this approach is that the training objective is aligned with detecting anomalies. As result, foundation models lack explicit knowledge of what constitutes an anomaly. This issue is further amplified by the fact that anomalies heavily depend on the domain. Further fine- tuning on the pre- pre-trainn training task does not address this misalignment, as it only enhances forecasting or reconstruction performance rather than the models' anomaly detection capabilities. We therefore argue that effective TSAD with TSFMs may benefit from end-to-end fine-tuning of the founda- tion models using labeled anomalies. This is analogous to how pre-trained L.LMs are fine-tuned for sequence labeling tasks specifically. such as part-of-speech tagging or named entity recognition (De- vlin et al.) 2019 Tjong Kim Sang & De Meulder 2003). Even more, TSFMs potentially only require limited number of anomalous events to adapt their learned representations of time series to domains. This is especially important for TSAD, in which often costly to collect large sets of labeled anomalies (Chandola al. 2009). A comprehensive study of this paradigm is beyond the scope of the present work, but we highlight it as a promising direction for future research.
7 CONCLUSION
In this work, we showed that the current approaches apply TSFMs to TSAD are not effective. Our analysis shows that anomalies are not consistently harder to reconstruct or forecast with TSFMs, un- dermining the key assumptions on which existing methods rely. These findings hold irrespective of model size, context window length, or overall peconstructionfforecattii performance. Specifically, we found no significant difference between TSFMs and two simple baselines that were selected to mimic their predictions for TSAD. We also emphasized the importance of visually analyzing both data and algorithm outputs, because aggregated evaluation metrics often hide the true strengths and weaknesses of an algorithm. Looking forward, suggest three alternative directions to leverage the potential of TSFMs in TSAD: (1) ensemble short and long forecasting horizons, (2) detect anomalies in the hidden representations, and (3) fine-tune using labeled anomalies.
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ACKNOWLEDGMENTS
This research supported by the Flemish government under the Flanders AI Research Program, by Flanders Make, the strategic research centre for the manufacturing industry, and by Internal Funds KU Leuven (C3/23/047). This work used the resources and services provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation Flanders (FWO) and the Flemish Government.
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A DECLARATIONS
A.1 USE OF LARGE LANGUAGE MODELS
We used Large Language Models (LLMs) exclusively for polishing the All research ideation, literature review, experimental design, implementations and analysis were carried out by the au- thors. The LLMs did not generate any scientific content or influence the scientific findings this manuscript.
A.2 CODE AND DATA AVAILABILITY
Code. All our scripts to load the time series, detect anomalies, and evaluate the perfor- mances publicly available at http:p/s//ggii.oooo....eeeeeae/s
pus-brugge at p http::/eee2222mmmmmmmceeeoeeoe timeseriesfoun
dationmodelsad/ Additionally, we :
TSB-AD h1tp4///eeee//e/t./ooooo///a/oa0 MOMENT (http
8: o9/24hhhh...ooooo........sssasa1
TimesFM httpa//ww.gaa.////o.o..////////a
Chronos Time- Time- MoE bee.:tttee//ggttta.ccc////////id TSPulee (https:
// //g.tnasccco///nnnncaaattaaaaaat
2. Experimental results. We added the experimental exprrnitaatrceiiintnnaaaallrrrio results in supplementary material, but these are also available /ttp://///....a.a..aaaaaaaa////. httpapcpccpbllaclioiiiooooooooo
timeseries foun dationmode sad/ In addition, we also have the raw anomaly scores, reconstructed subsequences, and model forecasts available in the online repository.
s s-brugge/átai
3. Data. We relied on publicly available data (Liu & Paparrizos, The raw data files are available https: /tt:::///ttttt com/ hwtt/ThDDaattrrgee http://.ww./......ooooooo////t/.
v-file4cataset and the train-test split at http:://ttttb
AD / http://reemmaaan/tttaatttsss///s
List/
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B DESIGN CHOICES FOR TSFMS
We briefly introduce variations in the design choices for TSFMs, focusing on the four Transformer- based families examined in our experiments: MOMENT (Goswami al. 2024). Chronos (Ansari et al. 2024abb) TimesFM (Das et al. 2024), and Time-MoB (Shi et al. 2024).
B.1 NORMALIZATION
One difference between natural language and time series is that while the number of words language is finite, a data point in time series can, in principle, take infinite, unbounded values. To handle any time series, TSFMs include a normalization step during preprocessing and a mirroring de-normalization step in postprocessing.
In this regard, Time-MoF (Shi et al. 2024 MOMENT Bolt (Ansari et al., 2024b), and TimesFM (Das et al. 2024) all use Z-score normalization, which typically transforms each context window into a series with mean of O and a standard deviation of 1. Notably, Chronos-t5 (Ansari et 2024a), the precursor of Chronos-Bolt employs mean scaling, which divides each data point in the context window by the mean of the window.
Gossami et al, 2024). Chronos-
B.2 TOKENIZATION & EMBEDDING
For LLMs, the process converting a sequence of text into a sequence of embeddings consists of two steps. First, the sequence is broken down into smaller, discrete units called tokens, which can be words or subwords, the tokenization step. The embedding step then converts these tokens into vectors called embeddings, which capture both syntactic and semantic information of the tokens.
Among popular families of Transformer-base like LLMs. In Chronos-t5, each data point is mapped to a discrete token through uniform binning, with an embedding learned for each token. In contrast, Time-MoF. directly maps each data point embedding using learned SwiGLU layer (Shazeer (2020), while MOMENT, TimesFM, and Chronos- Bolt segment data points into equal-length subsequences called patches, which are then mapped embeddings via learned linear projection residual block.
TSFMs, only Chronos-t5 employs discrete tokens
B.3 MODEL ARCHITECTURE
The vast majority of LLMs are based on variants of the transformer architecture introduced in 2017 (Vaswani et al. 2017). A transformer typically transforms a sequence input embeddings into another sequence of output embeddings, enabling different tasks such next-token prediction or sequence reconstruction. The original transformer architecture included both an encoder and a de- coder. However, architectures featuring only encoder (Devlin et al.) (2199) only decoder (Radford et al.] 2018) have since been proposed, with the decoder-only architecture becoming the de facto choice for the latest L.LMs.
The four families of Transformer- based TSFMs examined in our experiments fall into the three categories of model architecture mentioned above. Chronos uses encoder-decoder architecture, MOMENT uses an encoder-only architecture, and Time-MoE and TimesFM both use a decoder-only architecture.
B.4 PRE-TRAINING TASK
Before being adapted to various downstream tasks, foundation models are pre-trained a single task using huge amount of training data in a self-supervised manner. For this pre-training task, Chronos, Time-MoE, and TimesFM, like the vast majority of current TSFMs, all use forecasting. More specifically, for the loss function in pre-training. Chronos uses the cross entropy loss for the next data point, TimesFM uses the Mean Squared Error for the next patch, while Time-MoB involves forecasting with different prediction horizons. On the other hand, MOMENT'S is the only large-scale TSFM, far know, that pre-trained on the task of sequence reconstruction.
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C
MORE EXAMPLES ON THE SIMILARITY BETWEEN TSFMS AND BASELINES
MMA
camaa
Reconstruction based TSFM vs. Baseline
Reconstruction TSFM Baseline
WWW VMmm Forecasting based TSFM Baseline
Forecasting based TSFM Baseline
(a) TTSB-AD-U 810 (Exathlon)
(b) TSB-AD-U 179 (SMD)
wwwp://wwwwww.woooooooooeaoaaaae Reconstruction-bssed TSFM Baseline
Reconstruction-oased TSFM Baseline
Forec Totecasting based TSFM vs. Baseline
Foreca wwwis-ttinssssee TSFM vs. Baseline
(c) TSB-AD-U 846 (OPPORTUNITY)
(d) TSB-AD-U 723 (YAHOO)
$2000
Reconstruction--aase TSFM vs. Baseline
Reconstrucion-based TSFM vs. Baseline
Forecasting ISFM Baseline
Forecasting based vs.
(c) ASB-AD-U 017 (NAB)
(f) TSB-AD-U 106 (WSD)
Recon www.ssttuutttonooo based T vs. Baseline
Reconstruction TSFM vs. Baseline
Forecasting TSFM Baseline
Forecasting-based TSFM Baseline
(g) TSB-AD-U 234 (SED)
(h) ASB-AD-U 539 (SMAP)
Figure 9: TSFMs and baselines give similar anomaly scores.
18

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Published as a conference paper at ICLR 2026
D DERIVATION FOR THE DECOMPOSITION OF ANOMALY SCORES GIVEN BY MOMENT
MOMENT z-normalization for the normalization and de-normalization steps in the pipelines shown in Figure As a result, the anomaly scores can be decomposed as the product of the variance of the window and the reconstruction MSEs of the normalized window. The derivation for this conclusion is as below, where T,r4 -, denotes the normalized context window, which is fed into the TSFM (ressonstruc)) for reconstruction.
14 == mean (Ti,4w--1)
std (T, (Tii+w--))
[/t[w/2]
MSF
-- Ti,
MSE
Thomm
1-+--1
(0; c) + 144 F(recommstuuc)) (T (,1+--11) (0+ 14)
MSE [Toom (T, (o; e),
(reconstruut)) ((iit=-1) (ot +
=(a
c)² MSE Trorm
1
(recomsttuutt) (TP,ttm-1))
~0. MSF
T mmorr 1i1+w-1>
(reconsttuut)) (T (Tittw-1))
Note that MOMENT's built-in normalizer adds small c = 10 to the true standard deviation to avoid division by zero, but its influence our conclusion here is negligible.
19

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Published as a conference paper at ICLR 2026
E COMPLETE EXPERIMENTAL RESULTS FOR RECONSTRUCTION-BASDI MODELS
1.0
Nomeal Atmaliaa
1.0
Nira
VU5 Pt: Mer 255
8.6
0.6
yex(9/%)
0.2
0.2
yox(1%)
0.0
VUS
0.6
ade c33
630
0.76
1.90
5.6
1.00 1.7
VUS-FR (NOMMNN--ase-255)
26
9:00 025 015
0:79
1.00
9.5 0.00 1/ 200
(a) MOMENT-base 64
(b) CROMENT-base-255
1.0
vU.S PA (von512)
0.8
your15)
0.4
0.0
0.0
0.6
0.6
0.8
VUS:PR INOMENTbase 512)
(c) MOMENT-base-511
Figure 10: One-to-one comparison between MOMENT-base and moving- variance across 350 time series in the TSB-AD-U evaluation set (left); Distribution of reconstruction MSEs of the normalized context windows (right)
1.0
Horeel
1.0
Horma
yoar7%
0.8
yoatl1
VUS-FR 2561
0.6
0.6
0.4
y-x668%)
0.2
(ox115%)
0.0
0.0
pex117%)
VUS PR IMOHENT large 64)
0.2
0.4
0.5
1.0
000 C25
0C0
6.5
2,00
175
206
VLS PS (MOMENT arge 2501
04
0's
000
025
053
035
1:00
335
150 L75 200
(a) MOMENT-MEEEEE -lageN--
(b) MOMENT-largee
1.0
Ascrniion
0.8
VIS-FR DWo 20040
0.8
9.6
0.4
0.2
yod! 70%)
0.2
yedf m%)
yxx)15%)
0.2
0.5
(c) HOMENTTlarge-5111
(d) MOMENT-large-1000 1024
Figure 11:
One-to-one comparison between MOMENT-lrge and moving- window variance across 350 time series in the TSB-AD-U evaluation set (left); Distribution of reconstruction MSEs of the normalized context windows (right)
20

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Published as a conference paper at ICLR 2026
F COMPLETE EXPERIMENTAL RESULTS FOR FORECASTING-BASED MODELS
Chronos-Bott-small-66
0.97
Chronos-8ot--mal-2556
Chrenos-olt-smaal 1:122
Chronos-Bolt-Beee Bolt-base644
Chrenos-Bor bolt-base 256
Chrenos Boll: base:512
Time MoE base 64
0.81
Time-Mpts 2.56
0.75
Time-MoÉ-base-512
0.74
Time-MoE-large-64
0.82
Time-MoE-large-256
0..55
Time-MoE-large-512
0.74
Times-FM-10066
0.96
Times-FM-L0-256
0.918
1.00
FM-2.0-64
0.84
0.82
2.0-512
0.88
Last:1
1.00
Last:3
1.30
Centered-5
0.71
0.8
1.0
0.00
0.25
0.75
1.00
1.50
VUS-PR Score
Aggregate Mean Absolute Scaled Error (MASE)
Figure 12: Boxplots (solid=median, dashed=mean) show that all TSFM variants achieve similar per- formance the TSB-AD-U evaluation set, despite differences in their forecasting performance in the same dataset. Baselines (Last-3 and Centered-5) achieve performance comparable with TSFMs.
Chronos Cronno--ol--maal-6 3.00 3.97 0.87
0.09 2.98 0.07 0.95 0:97 0.96 037 099 0.99 0.95 0.97 037 0.95 0.98 0.87 0.83 0.93 0.50
Chrons--utt-mmek-200 0.07 1.00 0.89
0.97 0.88 0.87 0.96 0.07 0.00 0.07 0.08 0.07 0.55 0.57 0.7 0.57 0.57 0.07 0.94 0.02 0.35
Chronce-Bof--mmmte 0.57 8.59 1.00 0.97 0.98 0.59 0.96 0.07 0.97 0.98 0.97 0.96 0.57 0.5 0.96 0.57 0.97 0.94 0.01 0.35
Chronos Bol: 64 0.69 0.97 1.00 0.98 0.96 0.97
8.98
0.97
Cironos Boit less 0.99
0.36
0.39 0.35 0.98
0.93 0.36
Civona-Bo b12e-112
b4se64
0.25
base
0.97 0.97 0.96 0.90
0.97 0.93
0.95
Tme-Mot-arge-566
0.98 0.97 0.97 0.97 99 1.00
97
0.91
0.95
MrE
0.8r 0.95 0.98 0.88 0.96 0.90 0.2 3.00
0.99/ 0.92 0.52 0.90
TImesFM-1 2464 2-64-0.6 0.96 0.86 0.95 0.88 0.97 0.97 0.90 0.97 0.9 0.96 0.97 1.00 0.08 0.7 0.99 0.98 0.98 0.93 0.92 0.30
-025
Tmm--NN-100220 0.97 8.97 0.37 0.98 0.98 0.98 0.97 0.98 0.99 0.97 0.7 0.98 0.99 1.00 0.99 0.99 0.59 0.99 0.94 0.98 0.97
Times mms--1M-1.05512 p.97 3.97 0.87 0.87 3.99 0.80 0.95 3.98 3.90 0.96 USY 0.96 0.97 0.99 1.00 0.97 3.98 0.89 0.83 0.83 0.97
Times PM 2.0 0.95 5.97 0.86 0.95 0.09 0.97 0.97 3.98 0.50 0.08 0.97 0.96 0.00 0.59 0.7 3.00 0.99 0.99 0.93 0.92 0.97
-050
Tmm--rt-..020 0256 0.98 8.57 0.87 0.99 0.99 0.98 0.97 0.98 0.50 0.97 0.97 0.96 0.55 0.59 0.9 0.99 1.00 0.99 0.93 0.09 0.96
mms--M-2.0512 0.77 0.97 0.97 0.97 0.98 0.99 0.96 0.97 0.98 0.99 0.98 0.99 0.99 999 000 0.93 0.92 0.96
Last
3 3-93
0.92
Centered-S
Otrmms Bulk ge
Cla pass Beitrraall-2 236
Carmess Nolhomalllf 212
Chosma dust destie
Gromssititee base-
Choonasiiin 532
Times des dandend
Timss Trmse Most rene-Mtta--a harptedd 251 2516
236
teree--teettteeeee 5222
Timas fo
Trmess 2550
Treres 10.522
Times Riga
Temien-Ptt D:356
Temes 8.5512
Lager
Lamid
Commondd
-1
Timee stall
Wime shott
Figure 13: All model variants and baselines show high pairwise correlation in their VUS-PR scores across 350 time series in TSB-AD-U, with the minimum correlation being 0.90.
21

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Published as a conference paper at ICLR 2026
G DATA CONTAMINATION FOR MOMENT
While none of the forecasting based TSFMs covered in our experiments was pre-trained on TSAD datasets (Ansari et al. 2024a Shi et al. 2024 Das et al. 2024), the only reconstruction-baseed one, MOMENT, did use some TSAD datasets in its pre-training (Goswami et al. 2024). One possible explanation for our observation that MOMENT reconstructs windows containing anomalies equally well (or poorly) when compared to windows containing no anomalies is data contamination. As shown in Figure 14 our observation still holds with time series coming from dataset not used in MOMENT's pre-training, refuting this explanation.
tomal
nomal
Horma
VS2
(a) MOMENT-base 64
(b) MOMENT 256
MOMENT-base-D5
Horma
50
terral
54
Homel
30
Sersd
Seeseet 211
(d) MOMENT-lare--64 (c) MOMENT-large2266
(f) MOMENT-lage-512
MOMENT-large- 1024
Distribution of reconstruction MSEs of the normalized context windows with 94 time series in the TSB-AD-U evaluation set, coming from datasets not used in MOMENT's pre-raining
Figure 14:
"WSD, Stock, LTDB, SED, TAO, NEK, CATSy2, TODS, Power, SWaT, Exathlon
22

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Published as a conference paper at ICLR 2026
H SECONDARY FINDINGS
We observed that in some time series, the higher VUS-PR scores achieved by forecasting-baser TSFMs, when compared with the -quared-difference baseline, stem from issues with the ground truth labeling and the metric's tendency to amplify minor differences. We illustrate this with representative case.
Figure shows time series from the TSB-AD-U evaluation set. All data points in this time series are equal to -I, except for one point which is equal to I. The ground truth indicates that an anomaly happened from 16 time steps before this deviating value, and lasted until 34 steps after it. For this series, TimeMoE-large-551 got a VUS-PR score of 0.51 while the score for the Last-1 baseline is only 0.05. Figure reveals the following problems:
Problematic labeling: is hard understand why the data points around the spike should also be labeled anomalous. In particular, why should there be more data points marked as anomalous after the spike than before the spike?
given the ground truth, it is hard to tell why TimeMoE should get than the Last-1 baseline when looking their anomaly scores plotted on linear scale (the second and the third subfigures). The reason only becomes visible when the anomaly scores from TimeMoE are plotted on logarithmic scale (the fourth subfigure). This reveals tiny bumps at the level of 10-s just to the right of the spike, where Last-1, contrast, yields exact zeros. Because VUS-PR (Paparrizos) ett al.) 2022), like its parent AUC-PR, only cares about the relative ranking of the scores rather than their actual values, these tiny bumps result significantly higher VUS-PR for TimeMoF.
Exaggerating metric: much higher performance
Our inspection identified 50 time series with problematic labeling in the TSB-AD-U evaluation set. The indices of the 50 problematic time series are listed in Table For 47 of them, the labeling issue involves anomalous labels (often arbitrarily) overlapping with normal regions, similar to the example shown in Figure
15
The remaining three, from the UCR dataset, exhibit off- off-by-one mislabeling.
Tieeeeta
TimakoElarge-122
Last-1
Sromaly
TimeMoE targe 912
Avemaly
-30
1000
2000
3003
4000
5203
6000
7000
8050
Figure 15: Case study with TSB-AD-U 534 (SMAP)
Dataset
Index
Problem
NAB
0-22
SMAP
245-261
SWAT
262
Anomalous labeling overlapping with normal regions
MSL
43, 45, 47, 48
SMD
68, 74
UCR
212, 221, 228
Off-by-one mislabeling
Table 7: Time series with problematic labeling in the TSB-AD-U evaluation set
23

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Published as a conference paper at ICLR 2026
Removing these instances further reduced the performance gap between forecasting based TSFMs and the
baseline, as shown Figure I6
Without Problematic Instances
1.0
y=x(85%)
1.0
y=x(86%
1.0
y=x(89%)
y>x(3%)
y>x(7%)
y>x15%)
VUS-PR (Last-1)
0.8
y<x(13%)
0.3
y<x((5%)
VUS-PR (Centered-5)
0.8
y<x(6%)
0.6
0.5
VUS-PR
0.4
0.4
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
VUS-PR (TImesFM-2.005522
VUS-PR (TimesFMM-2005512
VUS-PR (TinesFM-2.0-512)
With Problematic Instances
1.0
y=x(82%)
1.0
y=x(85%)
1.0
y=x(86%)
y>x(3%%
y>x(6%)
y>x(5%)
VUS-PR (Last-1)
0.8
y=x(15%)
VUS-PR (Last-3)
0.8
y<(((9%)
VUS-PR |Contered-5)
0.8
y<x(9%)
0.6-
0.6
0.6
0.4-
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6 0.8
1.0
0.0
0.2
0.4
0.6
0.8 1.0
(TimosFMM-2005521
VUS-PR (TimesFMM-2.05522)
VUS-PR (imosFM-2200512)
Figure 16: One-to-one comparison between TimesFM and the squared-difference baseline over the 350 time series in the TSB-AD-U evaluation set, with and without time series with problematic labeling.
24

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Published as a conference paper at ICLR 2026
1
ABLATION STUDY ON NORMALIZATION
Reconstruction-basee Models
1.0
1.0
y=x(86%)
1.0
y=x166%1
yox(79))
yox1200%
VUS-RR (Var644)
VUS-PR (Var-44)
yCx(7%)
VUS-PR D.S.PR.VVoS (Var-64)
y=x(149%)
0.6
0.6
0.6
0.4
0.4
0.4
y-x(9/%)
0.2
0.2
0.2
xox(2%)
(1x
0.0
0.0
0.2
0.2
0.4
0.6
0.8
VUS-PR (MDMENT-basS--64)
VUS-PR (bae-E))-
VUS-PR (MOMENT-bas--61)
window-wise Z-norm
window-wise Min-Max
global Z-norm
Forecasting-base Models
1.0
y-x185%
1.0-
y=x(85%) %)
1.0-
y-x(87%)
y>x(6%)
y>x(5%
y>x(5%)
0.8
y-xx(9%)
0.6
yxcx(9%%
0.8
y<cx(8%)
VUS-PR (Last-3)
VUS-PR (Last-31
0.6
0.6
0.6
VUS-PR
0.2
0.0
0.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
VUS PR (Time MbF base
VUS PR (Time MoE base 64)
VUS PR (Time MoE base
window-wise Z-norm
window-wise Min-Max
global Z-nom
1.0
y=x(85%)
1.0
y=x(85%)
1.0
y=x(85%)
y>x(6%)
y>x(6%)
y>x(6%)
VUS-PE (Last-3)
0.6
y=<((9%)
VUS-PR (Last-3)
0.6
y=x((9%)
VUS PR (Last-3)
0.B
y<<((9%)
0.6
0.6-
0.6
0.4
0.4
0.0
0.0
0.0
0.0
0.2
0.4
0.6
0,8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
[1iimeFF-2200-555 0-515)
VUS-PR [TimesFM--Z
0-512)
[TimesFFM-2
window-wise Z-norm
window-wisse Min-Max
global Z-narm
1.0-
y=x(86%)
1.0
y>x(6%)
0.6
y<x(88%]
0.6
VUS-PR (Last-3)
0.6
0.6
VUS-PR
y=x(85%)
0.2
y>x(111)
(5x(
0.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
VUSS-PR (Chronns--oolt Haaeee-oottt-hhete -ase--e44))))) window-wise Z-norm
VUSS-P (Chronos-th-baeee baaee--6- window Mean-scaling
Figure 17: When employing normalization methods other than window-wise Z-norm, the correlation with baselines diminishes for reconstruction- based models but persists for forecasting- models.
25

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Published as a conference paper at ICLR 2026
J STATISTICAL ANALYSIS
Table High correlation (with extremely low p-values) and small effect size between the VUS-PR scores of TSFMs and their corresponding baselines Moving-windor variance for Reconstruction- based TSFMs and Last-3 for Forecasting- TSFMs) over the TSB-AD-U evaluation set.
correlation
p-value
Cohen's
(TSFM)
(baseline)
MOMENT-base-6
0.412
0.414
0.994
-0.048
MOMENT-base-255
0,386
0,386
0.995
-0.005
MOMENT-has--11.
0,311
0,313
0,991
7.91E-308
-0.047
MOMENT-lange-6
0.427
0,414
2.90E-149
MOMENT-large2556
0.389
0.386
0.848
1.87E-98
0.016
MOMENT-large512
0.313
0.313
0.857
1.38E-102
0.003
MOMENT-large-1022
0.245
0.273
0.855
4.78E-101
-0.180
Mhronos-Bolt-smann
0.304
0.284
0.926
145e-149
0.153
0,294
0,284
0,920
7.27c-144
0.077
Mtronos-Bolt-smalke
0,295
6.71e-139
Chronos-Bolt-basee
0.299
0.284
0.937
1.29e-161
0.124
Maronos-Boit-bass-
0.293
0.284
0.916
9.76c-141
0.068
Mhronos-Bolt-bas--511
0.297
0.284
0.927
1.38e-150
0.102
Time-MoE-base-64
0,300
0.284
0,918
9.82e-142
0,121
Time-Mot-base-256
0,300
0,284
0,932
141c-115
0.132
Time-MoF-baas--51
0.299
0.284
1.07e-155
Time-MoE-large-64
0.304
0.284
0.899
3.00c-127
0.134
Time-Mob-large-256
0.297
0.284
0.918
7.17e-142
0.095
Time-MoF-large-512
0.303
0.284
0.923
8.18e-147
0.146
F1m.6-FM-110064
0.310
0.284
0,918
5.14E-142
0.194
Times-FM-L.0-256
0,305
0,284
0,934
8.05E-158
0.175
1im.0-2M-110-512
0,305
0.284
3.77B-157
0.172
Times-FMM-200-64
0.303
0.284
0.923
9.70F-147
0.144
Fimes-FM-220-256
0.304
0.284
0.926
1.56E-149
0.156
Times-FM-2.0-512
0.302
0.284
0.923
7.82E-147
0.142
On top of the results shown in Table 8 above, we also conducted non-inferiority tests based on Wilcoxon signed-rank, with which we can exclude the possibility (with at least 95% confidence level) that the TSFMs outperform the corresponding baselines by more than small margin (0.016 for MOMENT-large, 0.0008 for MOMENT-base, and 0.008 for all forecasting-
TSFMs).
Moreover, our key findings still hold when the metric is switched from VUS-PR to Event-based F1, as shown Table below. In parallel, for Event-based F1, the same non-inferiority tests show that we exclude the possibility (with at least 95% confidence level) that the TSFMs outperform the corresponding baselines by more than a small margin (0.025 for MOMENT-large, 0.0001 for MOMENT-base and 0.02 for all forecasting-basee TSFMs).
26

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Published as a conference paper at ICLR 2026
Table 9: High correlation (with extremely low p-values) and small effect size between the Event-based Fl scores of TSFMs and their corresponding baselines (Moving-window variance for Reconstruction-base TSFMs and Last-3 for Forecasting-basee TSFMs) over the TSB-AD-U eval- uation set.
correlation
p-value
Cohen's
(TSFM)
(baseline)
MOMENT-
0.495
0.487
0.985
3.29e-270
0.118
HOMENT-base-22
0.419
0.417
0,996
MOMENT-base-512
MOMENT-large-4
0.509
0.487
0.864
3.76e-106
0.106
MOMENT-laree225
0.465
0.417
0.794
1.24e-77
0.199
MOMENT-large512
0.383
0.345
0.809
1.95e-82
0.179
MOMENT-large-100
0.287
0.300
0.866
1.11c-106
-0.077
0.633
0.619
6.36e-95
0.062
Chronos-
Phonno-Bolttssall-255
0.636
0.871
6.89e-110
0.625
0.619
0.845
9.84c-97
0.028
Maronos-Bolt-basee
0.623
0.619
0.900
4.19c-128
0.022
Chronos-Bolt
0.626
0.619
0.871
5.89e- 10
0.034
Chronos-Bolt
0.621
0.619
0.868
5.29c-108
0.009
Time-MoE-
0.626
0.619
2.22c-117
0.039
Time-MoE-base-26
0.619
0.881
1.91e-115
0.162
Time-MMo--base-52
0.650
0.619
0.859
9.73e-104
Time-MoE-large-64
0.635
0.619
0.887
5.99c-119
0.084
Time-MoF-1arge-256
0,653
0.619
0.877
3.46c-113
0.172
Time-MoE-large-512
0.660
0.619
0.855
1.45e-101
0.193
Times-FM-L.0-64
0.632
0.619
2.04c-100
Timos--M--10-256
0.644
3.87e-107
Times-FFM-1.05112
0.638
0.880
4,23e-115
0.097
Times-FM--20-64
0.631
0.619
0.880
5.67e-115
0.062
-i120-2M-22002566
0.636
0.619
0.860
3.14e-104
0.080
Fimes-FM-200-52
0.633
0.619
0.860
3.51c-104
0.067
27

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Published as a conference paper at ICLR 2026
K COMPARISON WITH NON-TSFM TSAD METHODS
Table 10: Correlation VUS-PR scores over the TSB-AD-U evaluation set between non-TSFM
TSAD methods and the baselines. The scores of the non-TSFM TSAD methods directly taken from Liu & Paparrizos (2024). The window size of the Moving-winet wiindw Variance baseline (Var) is chosen be the same with the window size of the TSAD method comparison. When the TSAD method does not involve window size, it is compared with Var-64.
correlation with Var correlation with Last-3
Sub-IForest (Liu
2008
0.301
0.279
IForest (Liu 2008
0.534
0.628
Sub-T.OF (Breuni
2000)
-0.088
0.140
LOF (Breunig al. 2000
0.395
0.654
POLY [Li et al 2007)
0.795
0.436
MatrixProlile Yeh et al.
2016
-0.034
0.242
KShapeAD (Paparrizos & Gravano
2017
-0.107
0.092
SAND (Bonio] ef 2021
-0063
0.097
Series2Graph (Bonol & Palpanas
2020
0.464
SR (Ren al.
2019)
0.556
0.805
Sub-PCA (Agearwal]
2017
0.470
0.203
Sub-HIBOS CEliIBSSooollssss Golldteen & Dengel
2012
0.285
0.250
Sub-OCSVM (Schilkopi et 1999)
0.104
0.213
Sub-MCD Roussceuw & Dresssn
1999
-0.046
0.293
Sub-KNN Ramaswamy el al
2000
-0.045
0.075
KMeansAD Yairi ct 2001
-0.040
0.185
AutoEncoder Sakurada & Yain
2014
0.378
0.361
(Munir et 2018)
0.660
0.704
LSTMAD (Maloolia (MaMoooaalloo el 2015
0,518
0.587
TranAD (Tiil 2022
0.472
0.464
Anomaly Transformer
2022
0.415
0.491
OmniAnomall (Su 2019
0.494
0.435
USAD (Audibert et aL 4020)
0.615
0.277
Donut Xu el al 2018
0,580
0.527
TimesNet (iu ct al 2023
0.805
0.724
FITS (uu ét al. 2024
0.773
0.621
28

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Published as a conference paper at ICLR 2026
L FORECASTING HORIZONS FOR FORECASTING-BASED TSFMS
As depicted in Figure 18 under the forecasting- methodology, increasing forecasting horizon can improve TSFMs' TSAD performance for sequence anomalies, at the expense of performance deterioration for point anomalies. Furthermore, the optimal forecasting horizon correlates with the anomaly length, as evidenced by the peaks in Figure
0.9
anomaly length
emornely length
0.8
2-04
0.7
2-64
05-258
257-
g
0.6
25/-
65-256
0.5
0.4
0.3
0.2
0.1
100
200
300
400
500
100
200
300
500
forecasting horizon
forecasting horizon
(a) TimesFM-1.0-511
(b) TimesFMM-200-51
enoortelly length
anoomaal length
0.8
-----
0.7-
0.7
meen VUS PR
257-
63-258
0.6
05-256
257-
0.5
100
200
300
400
500
100
200
300
500
forecasting harizan
forerasting herizon
(c) Chronos-Bolt-smalll Bolt-smal--512
(d) Chronos-Bole colt-base-512
Figure 18: Mean VUS-PR scores with various forecasting horizons, evaluated on time series con- taining anomalies of different lengths within the TSB-AD-U evaluation set.
29

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Published as a conference paper at ICLR 2026
M PRELIMINARY RESULTS ON MULTIVARIATE TSFMS
100
200
(a) TSB-AD-M 001 (Genesis)
(b) TSB-AD-M 171 (SWaT)
100
2005
(c) TSB-AD-M 016 (MSL)
(d) TSB-AD-M 018 (Daphnet)
TSB-AD- 081 (LTDB)
(f) TSB-AD-M 193 (Exathlon)
(g) TSB-AD-M 124 (TAO)
(h) TSB-AD-M 156 (SMAP)
Figure 19: When employing the forecasting- based methodology for TSAD, multivariate TSFMs and the Last-3 baseline yield comparable anomaly scores. These scores were calculated the mean squared prediction error at each time step across all channels.
30


Reviews:
[{"review_id": "GNaYZoHZub", "reviewer": "Reviewer_tjti", "summary": "The paper investigates why time-series foundation models (TSFMs) underperform on time-series anomaly detection (TSAD) when used with standard error-based pipelines. The core empirical finding is that, for reconstruction-based use (e.g., MOMENT), sliding-window z-normalization makes the final anomaly score a moving-variance detector, since the normalized-space MSE is nearly constant across windows. For forecasting-based use, the paper shows single-step horizons can miss sequence-type anomalies. Across several TSFMs and baselines, simple one-line methods (moving variance, last-value/centered mean predictors) are competitive. The paper argues for (i) representation-space anomaly detection instead of error-based scoring, and (ii) more appropriate horizon settings.", "questions": "1. **Normalization ablations**: What happens under RevIN, MinMax, and global (non-sliding) normalization for MOMENT and at least one forecasting TSFM? Does the variance-collapse remain?\n2. **Model-wise derivations**: Can you provide an analog of the de-normalization argument for TimesFM/Time-MoE (z-scaling) or explain decisively why it does/doesn’t apply?    \n3. **Chronos/mean-scaling**: If Chronos underperforms, is it for the **same** reason? Please dissect with controlled experiments.\n4. **Representation-space TSAD**: Present systematic results (kNN/density/one-class SVM) across multiple TSFMs/datasets, not just point cases—does this consistently beat one-liners?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- **Clear mechanistic insight**: neat derivation tying de-normalization to $(\\sigma_i^2)$ and explaining the empirical collapse to variance detection.\n- **Diagnosis** of a widely used pipeline (sliding z-norm + error scoring).\n- **Useful negative results** that recalibrate expectations vs. simple baselines.\n- **Horizon analysis** highlighting why single-step forecasting misses sequence anomalies.\n- **Constructive direction** toward **representation-space** anomaly scoring.", "weaknesses": "- **Over-reach in generality**: conclusions for MOMENT/z-norm are at times presented as TSFM-wide without parallel derivations or tests for TimesFM/Time-MoE/Chronos/others.\n- **Fairness of scope**: several forecasting models did **not** originally claim TSAD capability; the paper risks attributing pipeline failures to **model families**.\n- **Framing**: occasionally reads as a critique of **TSFMs per se**, while the evidence mainly indicts the **error-based TSAD pipeline**.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "blcme8qYo0", "reviewer": "Reviewer_nWUb", "summary": "This paper critically evaluates the effectiveness of time-series foundation models (TSFMs) for anomaly detection, examining five popular model families: MOMENT, Chronos, TimesFM, Time-MoE, and TSPulse. The authors' main finding is that across different model sizes and context window lengths, these TSFMs perform no better than simple one-liner baselines—moving-window variance for reconstruction-based methods and squared-difference for forecasting-based methods. The results reveal that the fundamental assumption underlying these approaches—that anomalies are harder to reconstruct or forecast—does not hold for current TSFMs. The paper contributes a comprehensive empirical analysis demonstrating this limitation, emphasizes the importance of visualizing data and algorithm outputs rather than relying solely on aggregate metrics, and proposes three alternative directions for future research: combining multiple forecasting horizons, leveraging hidden representations for anomaly detection, and performing end-to-end fine-tuning with labeled anomalies.", "questions": "1. Could you clarify the conventions used in the box plots? Specifically, what do the solid and dashed lines represent—for instance, are they the mean and median?\n2. On small-scale univariate time series, a specialized baseline method may perform well, but identifying the correct baseline often requires data-specific pre-analysis. Are you able to identify without pre-analysis? The value proposition of a FM lies not in outperforming a perfectly tuned baseline, but in its ability to be applied universally to any dataset and achieve robust, reasonable results without such data-specific selection.\n3. It is worth testing if combining TSFMs with baseline methods improves performance. A lack of improvement would indicate that the TSFM may not be offering additive predictive power beyond the baselines", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. very important question to answer\n2. a lot of experiments on many TSFMs, covering both reconstructing and forecasting models\n3. agree that visualization in anomaly detection is important\n4. good, detailed analyses", "weaknesses": "1. Focus only on univariate time series. TBH, I don't agree with the argument in footnote 1 in page 2 because univariate is easier (so the baseline methods might work on univariate but not on multivariate) and TSFMs are trained on multivariate time series.\n2. Need to see tabularized results to compare baselines against TSFMs to be more convincing. (seems like TSFMs are still slightly better than baselines)\n3. Some of the TSFMs are not trained for anomaly detection, so comparison is not fair", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3ebbNTymw6", "reviewer": "Reviewer_2izu", "summary": "This paper critically evaluates five families of time-series foundation models (TSFMs)—MOMENT, Chronos, TimesFM, Time-MoE, and TSPulse—for time-series anomaly detection (TSAD). The central finding is that, across model sizes and window lengths, TSFM-based anomaly scores (reconstruction/forecast errors) often perform on par with simple “one-liner” baselines (moving-window variance and squared difference), challenging the assumption that anomalies are generally harder to reconstruct or forecast. The paper then proposes practical fixes: ensembling multiple forecasting horizons, detecting anomalies in hidden representations, and modest end-to-end fine-tuning with labeled anomalies.", "questions": "1. Normalization sensitivity: If replacing z-scaling/sliding-window normalization with RevIN or sequence-level scaling, do the “TSFMs ≈ one-liners” findings materially change?\n2. Horizon selection: Have you tried uncertainty-aware or autocorrelation-aware adaptive horizon selection instead of max-over-h ensembling? How does it trade off early detection vs false alarms?\n3. Label granularity: If evaluated with event-level metrics (e.g., tolerant windows) instead of point-level, do core conclusions persist?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- Originality: Moves beyond leaderboard reporting to interrogate the mechanism behind TSFM-for-TSAD, revealing why reconstruction/forecast error can collapse to simple variance/neighbor baselines.\n- Quality: Evaluates five prominent TSFM families under consistent setups (normalization, tokenization, architecture choices summarized), with direct, like-for-like baseline comparisons.\n- Clarity: Effective visualizations diagnose single- vs long-horizon failures and show how representation-space detection helps.\n- Significance: Practical guidance—multi-horizon ensembles, embedding-space detectors, light supervised alignment—can immediately influence TSAD deployments.", "weaknesses": "1. Data/setting coverage is limited. Experiments focus on univariate benchmarks; richer multivariate and domain-diverse evaluations (industrial, finance, sensors) would strengthen external validity.\n2. Scope is narrow. The study centers on TSFMs vs. simple baselines; a fuller comparison against strong classical/modern TSAD methods (e.g., change-point, matrix decomposition, graph-based multivariate models) under matched conditions would be informative.\n3. Statistical rigor. Many insights are qualitative; add paired tests, effect sizes, and multiple-comparison controls for key claims.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cveVKQ7R4o", "reviewer": "Reviewer_sn7v", "summary": "The paper investigates whether TSFMs deliver reliable gains for TSAD compared with simple statistical baselines. The study evaluates reconstruction- and prediction-based TSFMs across multiple window sizes and reporting metrics, and finds limited or inconsistent advantages over one-line baselines.", "questions": "Since density-based TSAD approaches [1–3] represent another important direction in anomaly detection, could density scoring offer a complementary mechanism for TSFMs?\n\n[1] Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series. ICLR 2022.\n\n[2] Detecting Multivariate Time Series Anomalies with Zero Known Label. AAAI 2023.\n\n[3] CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series. 2025.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The question is timely and practically important, given the rapid adoption of TSFMs and the community’s push for standard, reproducible TSAD evaluations. The experimental coverage across representative TSFMs, scoring paradigms, and window settings provides useful negative results and actionable insights.", "weaknesses": "- Regarding the core claim that TSFMs are not significantly better than statistical baselines, the paper relies on averaged F1/AUC-ROC without formal statistical testing.\n- Regarding methodological transparency and practical guidance, it lacks of parameter counts, FLOPs, peak memory, and energy (GPU-hours), and plot accuracy versus cost to justify when TSFMs are warranted over one-line baselines.\n- Regarding the explanation that prediction-based TSFMs underperform due to single-step horizons leveraging local anomaly leakage, the paper may need an ablation over the forecasting horizon H. It will be beneficial to plot anomaly-detection metrics as H increases from 1 to L/4 and L/2 (with L the window length) to verify if longer horizons better separate anomalous regimes.\n- Minor comments:\n    - typo “detecte anomalies” → “detect anomalies”\n    - typo “requires” → “require” (line 470)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)