Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction
Authors: Shiyan Hu
Jianxin Jin
Yang Shu
Peng Chen
Bin Yang
Chenjuan Guo
Venue: ICLR 2026 Poster (2026)
Keywords: multimodal time series; anomaly detection
Primary area: learning on time series and dynamical systems

Abstract: Time series anomaly detection plays a critical role in many dynamic systems. Despite its importance, previous approaches have primarily relied on unimodal numerical data, overlooking the importance of complementary information from other modalities. In this paper, we propose a novel multimodal time series anomaly detection model (MindTS) that focuses on addressing two key challenges: (1) how to achieve semantically consistent alignment across heterogeneous multimodal data, and (2) how to filter out redundant modality information to enhance cross-modal interaction effectively. To address the first challenge, we propose Fine-grained Time-text Semantic Alignment. It integrates exogenous and endogenous text information through cross-view text fusion and a multimodal alignment mechanism, achieving semantically consistent alignment between time and text modalities. For the second challenge, we introduce Content Condenser Reconstruction, which filters redundant information within the aligned text modality and performs cross-modal reconstruction to enable interaction. Extensive experiments on six real-world multimodal datasets demonstrate that the proposed MindTS achieves competitive or superior results compared to existing methods. The code is available at: https://github.com/decisionintelligence/MindTS.

Published as a conference paper at ICLR 2026
TOWARDS MULTIMODAL TIME SERIES ANOMALY DE- TECTION WITH SEMANTIC ALIGNMENT AND CON- DENSED INTERACTION
Shiyan Hu', Jianxin Jin', Yang Shu', Peng Chen', Bin Yang', Chenjuan Guo East China Normal University
{syhu, jxjin, pchen pehen]/stu..o estu..suu...cuu www.eenwuuuuuuuo.oooo cn (yshu, byang, ejguo) edase. ecnu. ecnu.edu.co cn
ABSTRACT
Time series anomaly detection plays critical role in many dynamic systems. Despite its importance, previous approaches have primarily relied on unimodal numerical data, overlooking the importance of complementary information from other modalities. In this paper, we propose a novel multimodal time series anomaly detection model (MindTS) that focuses on addressing two key chal- lenges: (I) how to achieve semantically consistent alignment across heteroge- neous multimodal data, and (2) how filter out redundant modality informa- tion to enhance cross- cross-modal interaction effectively. To address the first challenge, we propose fine-grained Time-text Semantic Alignment. It integrates exogenous and endogenous text information through cross-view text fusion and multimodal alignment mechanism, achieving semantically consistent alignment between time and text modalities. For the second challenge, introduce Content Condenser Reconstruction, which filters redundant information within the aligned text modal- ity and performs cross-modal reconstruction to enable interaction. Extensive periments on six real-world multimodal datasets demonstrate that the proposed MindTS achieves competitive or superior results compared existing methods. The code is available at: http://ggnnbccoommdccccoonllllne
INTRODUCTION
Time series anomaly detection identifies anomalous events that significantly deviate from the ma- jority within time series data. It has been widely applied in various domains, including healthcare monitoring, financial fraud detection, and network intrusion detection (Li et al. 2021 Yang et al., 2023b Boniol et
2022 2024 Sylligardos al. 2023).
In various real-world scenarios, data often exists in multimodal form, such as time series (Liu
& Paparrizos] 2024 Dai et al. 2024). text (Enevoldsen al. 2024 Chen al. 2024b). im- Bhunia et al.) 2024), and videos (Li et al. 2024 Chen al. [2244a] He et al. 2024), which collectively serve as complementary heterogeneous
ages (Costanzino et al. 2024| Zhou et al. 202miaa
formation sources. Among these, the text modality, which contains contextual descriptions and provides rich background information for time series, is easy to obtain due to its wide availability. For instance, financial experts combine transaction data on stocks with reports and policies to detect market anomalies. Despite this, time series most existing anomaly detection models remain confined to unimodal numerical frameworks (Yang et al. 2023c Waang al. 2023] Song et al. 2023 Shentu jet al.) [025] Wu et al.] 2025), overlooking the potential utilizing multimodal data. Therefore, building multimodal time series anomaly detection models becomes natural and necessary step forward. In this work, we focus on the time series and text modalities, rather than aiming to build universal multimodal framework that also handles image video modalities. This focus raises key research question: how can we effectively integrate text information and time series?
Since different modalities reside in distinct semantic spaces, achieving precise alignment between text and time series is crucial for leveraging textual information effectively. A straightforward ap-
"onreiponningg authors

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Figure 1: (a) LLM-based methods generate endogenous text from time series without incorporating exogenous information. (b) Exogenous methods incorporate text information by retrieving background knowledge from the web. The absence of connecting lines indicates that the two modal- ities are not aligned. (c) MindTS employs cross-view fusion to ensure semantic consistency between the exogenous text and the time series, enabling more precise alignment across modalities.
proach (Jin et al.] 2023 Zhou et al., 2023 Gruver al.] 2023 Cao et al. [023] Kowsher et al. 2024) is to generate endogenous fext from the time series itself using large language models (LLMs), which naturally ensures modality alignment (Figure Tap. However, such text typically offers limited semantic richness, as it can only capture intrinsic patterns within the time series. To address this limitation, recent studies (Liu et al. 2024b, Li et al. 2025b) have explored incorporating exogenous text, such news reports or documents, as external contextual information. However, the effective- ness of such methods heavily depends on the quality of exogenous text. The external information sources are often scattered, making semantic alignment with the time series inherently difficult (Fig- ure Therefore, a key challenge is how to incorporate informative exogenous text while ensuring semantic consistency and alignment with the time series.
Moreover, while text provides complementary information to time series, it may also introduce redundant content that hinders anomaly detection. Existing multimodal time series methods perform direct fusion strategies (Liu et al. [024b) Jii et al.) (202)), assuming that all text information is equally useful. This overlooks the lengthy details or irrelevant descriptions within the text modality, which may dilute the contribution of genuinely informative content. In natural language processing, recent approaches (Cha et al., 2023] Liang et al.) (023) filter text representations using techniques such random masking or by applying random semantic parsing functions, such as paraphrasing, summarization, or translation, to perturb the text for filtering (Ji et al. 2024). However, when applied to multimodal time series, such strategies fail to consider the relevance of text content the time series, which may result in high-value text information being randomly masked while low-value text information is retained. Therefore, another key challenge is how to mitigate the impact of redundant content on cross-modal interaction through an effective filtering mechanism.
To address these challenges, propose MindTS,, Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction. Specifically, we propose a fine-grained time- text semantic alignment module that divides the text into two complementary views: exogenous text and endogenous text. The exogenous text contains background information from external sources, suitable for sharing across different time steps. In contrast, the endogenous text is derived directly from the time series, exhibiting time-specific characteristics correlated with temporal patterns. To achieve semantic consistency alignment between time-text pairs, we apply cross-vier fusion to in- tegrate the complementary strengths of the two text views. The resulting fused text is further aligned with the time series (Figure Furthermore, we propose a content condenser reconstruction mech- anism to filter redundant text information and enhance the effectiveness of cross-modal interaction. Specifically, given aligned text representations as input, the content condenser filters out redundant information from the text by minimizing mutual information, resulting in condensed text representa- tions. The condensed text representations are then used to reconstruct the masked time series, which strengthens cross-modal interaction. The contributions summarized as follows:
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We propose novel fine- fine-grained time-tex semantic alignment method that jointly exploits both exogenous and endogenous text representations time patches. The exogenous text introduces external background knowledge, while the endogenous text captures specific characteristies directly derived from time series. By integrating these two complementary text views, our approach ensures more precise semantic alignment with text and time series.
We propose a novel method, content condenser reconstruction, to filter redundant textual information. By performing moosl reconstruction of time series from condensed text, the content condenser reconstruction enhances interaction between modalities.
Our proposed multimodal anomaly detection model, MindTS, has been extensively evalu- ated on multimodal datasets. Compared with existing unimodal baselines and multimodal time series frameworks, MindTS achieves competitive superior performance.
2 RELATED WORK
2.1 TIME SERIES ANOMALY DETECTION
Time series anomaly detection has been extensively studied, and existing methods can be broadly
categorized into non-learning (Breunig al.) 2000; Goldstein & Dengel 2012 Yeh et al. 2016). classical machine learning (Liu et al.
2008 Ramaswamy et al. [2000] Shyu et al. 2003] Yair et al.
2001), and deep learning (Xu al. 2021; Deng & Hooi 202 Yang et al. 2023c| Shentu et al. 2025 Hu et al. 2024 Qiu et al., 2025al Xu et al.] 2024] Miao et al. 2025). Deep learning methods and contrastive learning-base DADA (Shentu et al. 2025) adopts a dual-adversarial decoder framework to reconstruct both nor- mal and abnormal series, where abnormal samples are expected to yield high reconstruction errors. GDN (Deng & Hooi, 202T) couples structure learning with graph neural networks by using atten- tion over neighboring sensors to forecast values, and derives anomaly scores from prediction errors. DCdetector (Yang al.) (023c) is the first to introduce contrastive learning into time series anomaly detection. It maps samples into shared embedding space, where normal points exhibit strong cor- relation with others, while anomalous points show weak correlations (Yang et al. (023a). Although these methods have achieved impressive performance in unimodal time series anomaly detection, they often overlook the rich semantic information available in other modalities, which limits their robustness in complex real-world scenarios.
can further divided into reconstruction
prediction-
2.2 MULTIMODAL TIME SERIES ANALYSIS
Multimodal approaches mainly exploit time series and textual information to enhance the robust- ness and effectiveness of time series analysis. Unlike traditional unimodal time series methods, multimodal time series analysis (MMTSA) presents greater challenges due to the complexity of
cross-modal interaction and heterogeneous data integration. With the recent advances in LLMs,
mainstream research in MMTSA (Liu et al. 2024b; Pan et al.] [2244] Liu et al., 2024c Kowsher ctt al. 2024, Wang et al., 2025) has focused on transforming time series data into text or image formats and feeding them into LLMs or vision models, respectively. These approaches typically employ a multimodal fusion network to integrate information across modalities and boost overall model performance. For instance, LLM-Mixer (Kowsher al. 2024) decomposes time series into seasonal and trend components, and feeds them along with textual prompts into a frozen pre-trained LLM. The LLM then generates predictions by leveraging both semantic knowledge and temporal structure. Time-MMD (Liu et al. 2024b) attempts to incorporate exogenous text to improve time series analysis tasks. However, exogenous textual sources are often scattered and weakly correlated with the semantics of specific time segments. Relying solely on hard alignment through temporal step synchronization overlooks deeper semantic associations between time series and text. Further- more, text data often contains much redundant content. Without appropriate selection mechanisms, cross-modal interaction may introduce semantic redundancy, which hinders the identification of anomalous patterns. To address these issues, our proposed method achieves precise alignment be- tween semantically consistent time-text representations by integrating exogenous and endogenous text information. Moreover, introduce mutual information minimization mechanism and cross- crossl reconstruction strategy to achieve text compression and modality-Ieve time series re- construction. These strategies improve the model's ability to identify anomalous patterns.
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(d) Cress model Reconstruction
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Figure 2: MindTS overview. Given an input time series X, we first apply instance normalization and patching, then encode the patches using time encoder. (a) Each patch generates its corresponding endogenous text O. Along with the input exogenous text C, both views are encoded and (b) fused via cross-view fusion to obtain fused text representations Ziex- Time and text representations are then semantically aligned via a multimodal aligmment layer. (c) To mitigate textual redundancy, the aligned text is compressed using content condenser. Finally, (d) the condensed text Zeon is used to reconstruct the masked time series, enhancing cross- modal interaction.
METHODOLOGY
Given input time series of length T as X (II....,IT) E RTXD, where D is the number of features. Traditional unimodal time series anomaly detection outputs = (Ui,-.., E {0,1)² where 1/- 1, & 21, T), indicates that timestamp is identified as anomaly. In the task of multimodal time series anomaly detection, we consider the time series data with an additional text modalities. Here specifically focus fusing time series modality X with a text modality, where the exogenous text modality is represented as sequence of length S, given by C= (CI..., .......co. cs) E R.S. Similar the unimodal time series anomaly detection, the problem of multimodal time series anomaly detection also determines whether ye is an anomaly or not.
3.1 OLE FRAMEWORK
In order to resolve the problem of multimodal time series anomaly detection, we propose the model MindTS, as illustrated in Figure [ This model provides an anomaly identification mechanism from a cross-modal perspective based on the input time series and text.
The time series is first input instance norm layer perform instance normalization and channel- independent processing (Ulyanov et al. 2017 Kim et al. (021). then the result is output to patching time encoder layer for the fine-grained modeling of patches. By the widely used time transformer (Cisstea et al. 2022 Nie et al. 2022), the following results are derived through the time encoder:
P {P) time: time
2
P
Patching(> X), Hume TimeEncoder(P))
where pi time E RexD denotes the i-th patch of X, Hime E Xdmmaed, N TT -1/p is the total patch number, is the patch size and is the horizontal sliding stride. Furthermore. endogenous text O {o', 0² ..., ..., are generated for each patch, where o' denotes the text generated by LLMs using specifically designed prompts based on the i-th patch. In addition, X processed through patching & masking and sared-weight time encoder to obtain the masked time representation, denoted as Hiie-
Next, endogenous text O and exogenous text C are fed into text encoder to model their text repre- sentations H and H. respectively. Based on them, the cross-view text fusion layer is employed
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to capture semantic dependencies, resulting in the text representation as Zua which is semantically correlated with the corresponding patch representation Hime Subsequently, a multimodal alignment layer is employed align semantically consistent time-text pairs.
Finally, based on the aligned text representations Ziesi the content condenser filters out redundant text, giving rise condensed text Zoon- It is then utilized together with the masked time representa- tion Hime by cross-modal reconstruction, which produces the final output X and facilitates modality interaction. Reconstruction error is the anomaly score.
3.2 FINE-GRAINED TIME-TEXT SEMANTIC ALIGNMENT
The fine-grained time-text semantic alignment consists of three components: 1) the endogenous text generation, 2) the croxs-view text fusion and 3) the multimodal aligument strategy. Specifically, cross-view text fusion is designed to integrate text from different views (endogenous and exogenous text), helping enhance semantic consistency between text and time series. The multimodal alignment aims to guide the alignment between time representations H Hime and text representations Zuex from cross-view text fusion within unified space.
Endogenous text generation. To mitigate semantic drift and output uncertainty in directly con- verting time series into natural language (Kowsher et al.] [202] Jin et al.] (223), we design unified endogenous text prompt templates (e.g., mean, extrema, trend) and apply them to each patch to gen- erate corresponding endogenous texts. In this case, the limitations caused by generating a single global prompt can be avoided, and the dynamic property of the time series is matched.
The text encoder leverages open-source LLMs (Liu et al. 2024aa Radford al. 2019) to encode the endogenous text, resulting in the time-specific text representation H IR Xdmondes as follows:
H TextEncoder((fo) oz, .?....,))), text
where of E Rexddodd e is the LLM's vocabulary size. This prompt leverages the semantic knowl- edge of LLM to enhance semantic consistency with the time series. On the other hand, to fully ex- ploit the exogenous text C information, we treat it as a shared text across all patches. This approach ensures that the model does not lose background context due to the limited scope of individual patches. Similarly, the exogenous text encoded by text encoder, resulting in H E R glx doodell
Cross-view text fusion. To leverage the rich background knowledge in exogenous text and the strong semantic relevance of endogenous text to time series, MindTS integrates text information from endogenous and exogenous text views, introducing background knowledge while enabling precise mapping to specific patches. Specifically, we adopt cross-view attention mechanism that can selectively extract complementary information from two text views. To enhance semantic con- sistency with the time series and extract the most relevant background information, we use the endogenous text He as the query and the exogenous text H fused text representation Zeet- This process is expressed as:
text as the key and value to obtain the
Zuext FeedForward (Zuex) )):
Zex LayerNorm
(3)
- LayerNorm H Text (trossAtt(HH.. Text
H lext H H(m)),
(4)
where Zuext is intermediate variable. LayerNorm(.)) denotes layer normalization as widely adopted in (Vaswani al. 2217 Qiu et al., [2025c] Chen al., 2024c). FeedForward( denotes multi-layer feedforward network, and CrossAttn(Q, K, V) represents the cross-attention layer.
Multimodal alignment strategy. Time series manifest as continuous signals with strong temporal dependencies, while text is discrete, making semantic alignment between the two modalities dif- ficult. Traditional methods (e.g., add or concatenation) fail to capture the semantic alignment. To address this, we employ contrastive learning to explicitly align the two modalities, enhancing seman- tically consistent alignment by pulling positive pairs (aligned time-text) closer and pushing negative (unrelated ones) farther. Specifically, the similarity matrix between the two representations, Hime and Ziex is indicated as follows:
(b) time (
k
1 time? Zi
KTr
k( hh
time 2iri)
E NRN
(5)
N time?
k(hN ume?
N 4
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where k(.,) denotes the similarity between time and text representations. Jf g. the khhume z(ex) identified as a positive pair. Therefore, multimodal alignment loss MA is defined as:
1
N
exp (k(himme Aieu)TT)
N
cxp (me TTx
MA = 2NV
log
log
Σg exp (b(b(imee =lEu)/))
Σド=1 exp (k(b) Beex)
N
9=1
(6)
where T denotes the temperature.
3.3 CONTENT CONDENSER RECONSTRUCTION
As shown in Figure 2 the content condenser reconstruction includes: 1) the content condenser, and 2) the cross-modal reconstruction. Specifically, after aligning fie-grained representations that are semantically consistent across modalities, the content condenser filters redundant text information via masking. It utilizes the condensed text representation to reconstruct the time series.
Content condenser. Inspired by the Information Bottleneck (IB) principle (Tishby et al., 2000 Tishby & Zaslavsky, 2015), we propose the content condenser filter redundant representations based on the aligned text representation Ziext This process produces a condensed text representation
Zeon while preserving essential information for time series. Formally, the objective of finding the optimal condensed representation Zoon is defined as:
Zeon
arg
min [(zexti Zoon) + R(X, Zeon), ((Z ZZgua)
where T(::-) denotes the mutual information between aligned and condensed text representations. Minimizing it encourages the model to learn more compact representations. R(-,) -) denotes the reconstruction objective. Based on this, we introduce cross-modal reconstruction ensure the con- densed text retains sufficient information to recover the time series X.
Specifically, given the aligned text representations Ziex. we use an MLP to compute probability matrix V [ binary mask F Bernoulli (V) E {0, 1} is then sampled, where the higher the value of th. the more likely it is to sample Fi = 1. The condensed representation is obtained enable gradient propagation during sampling, we adopt the straight-through estimator trick (Jang et al. 2016).
as Zeon = Ziest O F, where @ the element-wise multiplication.
In order to control the marginal distribution of condensed text, thus regulating the condensing level, from the idea of latent distribution with variational auto-encoders, we introduce the distribution G (Zeon) ~ II; im Bernoulli()) subject to hyperparameter μa E (0, 1). By adjusting the value of μ,, we can restrain the condensing degree of the proposed model. To quantify the mutual information, the following lemma is proposed to get the upper bound before building the loss function.
Lemma 1. For the mutual information I [ZZrexti Zoon), there exists the following tight upper bound that can approximate its value:
T(Zuexi Zeoa) S Ryuess (TP
(8)
where KL(() denotes the Kullback-Leible (KL) divergence, defined as KL(((=)II ExP(x) P(x) log P(x)
G(x]' PP-) is the probability distribution and G(.) is a variational approximation. The proof is given in Appendix B
Utilizing the upper bound in Lemma Log as follows:
I
we can compute the KL divergence to obtain the loss function
N
Vi + (1
the log 1 2/ μ
Loe
th log
Another issue is that the condensed text might possess large difference between the i-th patch and the (i+ 11th patch, which results in the discontinuity and instability of the condenser reconstruction. To avoid this problem, we introduce Φi = (0 - )2 to compute the mask score difference of two Bernoulli samplings. Then, LsM
Σ N- b=1 is proposed to guarantee the smoothness of condensed text representation, ensuring stability in the learned features. In summary, the loss of the content condenser module is defined Ler. Loe Lsm-

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Cross-modal reconstruction. To enhance cross modal interaction, straightforward approach would be to perform time series reconstruction directly from the entire time series and the con- densed text. However, as the time series itself contains abundant information for reconstruction, this process cannot fully encourage the model to capture deeper cross-modal dependencies. To address this, we design more challenging objective: time series reconstruction with the random masked time series X and condensed texts Zeon. This design strengthens cross-modal dependency and en- courages the condensed representation to preserve richer time series-related information. As shown in Figure (d). X is processed by patching & masking to obtain X, which is then encoded by time encoder to produce Hume- The encoder shares weights with another time encoder. Given the inputs Hfime and Zom, the reconstructed output X IRT TxD is obtained X = Projection (Urr). and Urr is denoted as:
UTT FeodForward (Hime + CrossAttn (Hume: Zo L
(10)
where Ze.. = MSA (Zoon: Zeons Zom) denotes the self-attention layer. The cross modal reconstruc- tion function is formalized as:
CREE
x
X
(11)
3.4 JOINT OPTIMIZATION AND INFERENCE
Our total loss L primarily consists of three components: the multimodal alignment loss LM the condenser loss based condensed text LCL, and the cross modsl reconstruction loss Liee- There- fore, the proposed total loss function written as:
L LA LCL + LRee:
(12)
During the inference stage, the anomaly score at the current timestamp is computed based on the mean squared error between the time input X and its reconstructed output.
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Datasets. We conduct experiments using 6 real-world datasets (Weather, Energy, Environment, KR, EWJ, and MDT) to assess the performance of MindTS. Each dataset contains both numerical time series and corresponding exogenous text. More details of the datasets included in Appendix A.
Baselines. We extensively compare Mind'TS against 17 baselines, including (1) LLM- based meth-
ods: LLMMixer (LMixer) (Kowsher et al. 2024), UniTime (UTimee (Liu et al. 2024c)) GPT4TS
(G4TS) (Zhou et al. 2023); (2) Pre-trained methods: DADA (Shentu et al. 2025), Timer [Liu et al.
2024d UniTS (Gao al. 2024); (3) Deep learning-based methods: Modern (Modern) (Luo &
Wang 2024), TimesNet (1 T'sNet) (Wu et af. 2023). DCdetector (DC) ( (Yang et af.) (023c) Anomaly
Transformer (A.T.) (Xu al., 2021). PStchTST (Patch) (Nie et al. (2022), TranAD (Tuli al. 2022).
iTransformer (iTrans) (Liu et al. [023): ; (4) Non-learning methods: PCA (Shyu et al.
2003), FForest
(IF) (Liu et al. 2008), , LODA (Pevný 2016). HBOS (Goldstein & Dengel 2012). 2012).
Further details
concerning baselines are available in Appendix[AL A.2
Metrics. We adopt Label-based metric: Affiliated-Fi -l-score (Aff-F) (Huet et al. 2022) and Score- based metrie: VUS-PR (V-PR) (Paparrizos et al., 2022), VUS-ROC (V-ROC) (Paparrizos et al.) 2022] as evaluation metrics. We report the algorithm performance under total of 16 evaluation metrics in the Appendix
A.3 More implementation details are presented in the Appendix A.4
4.2 MAIN RESULTS
We evaluate MindTS with 17 competitive baselines on real-world datasets, as shown in Table I MindTS achieves state-of-the-arr (SOTA) performance across all datasets under the Aff-F, V-PR, and V-ROC metrics, which demonstrates that MindTS effectively combines multimodal data to detect anomalies. We further incorporate the 11 recent methods that perform well, shown in Table I into the multimodal time series framework MM-TSFLib (Liu et al. (024b)) as reported in Table 2 MM-TSFLib integrates textual information by performing linear interpolation between the output of time series models and bag-of words-basee text embeddings. Although this framework provides simple yet effective way to incorporate text, MindTS achieves the best or most competitive results

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Table 1: Results of MindTS compared with unimodal and LLM-based methods on six real-world datasets. These methods only use the time series in the dataset. MindTS leverages both endogenous and exogenous text together with time series. The best results are highlighted in bold, and the second-best results are underlined.
Dataccts
Metric
MixdTS
DADA
LMiner
UTine:
G4TS
Moder
ToNet
DC
A.T.
Palch
TruNAD
ITrm
PCA
LODA
AES
K3.65
73.68
76.65
75.46
16.17
23.56
31.53
90.58
43.90
-9.32
77.17
71.51
75.37
61.91
5c.06
$3.55
47.70
Weatter
V.P8
67.45
36.00
43:47
51 1.90
63.21
15.55
32.1
00:00
16:35
19:17
53.00
53.06
42.56
47.15
$5.11
45.5%
AP-2
76.37
6:36
55.85
41 96
69:30
613
06:37
70:33 76
66:00
13:17
-1.19
05.85
12.00
20333
47 65
62:03
63:55
$5.85
Easrg:
32.85
20-4
13.37
41.63
41.57
V.800
76.41
$3.9
15
54.31
56.37
61106
55.63
AE-F
85:29
11.35
71
13
30:01
63.04
61.41
74.43
55.63
06.50
18:45
V-B0I
15.75
37.80
41.
10.30
92.10
06:79
37.97
41.25
$1.98
993.0
14:20
73.33
17.08
46.20
53.80
51.10
$6.13
58.55
50.55
56.44
79.52
53.11
00:35
02.1%
ViBg
53.15
1:34
27.59
V-K0I
39.96
$3.79
775 55
75.09
71.55
07:51
10.
75.00
$3:01
-1.97
74.65
km
76.12
13:51
64130
$9.99
67:11
EWI
50.42
43.36
13:21
12.39
53.13
92.32
08.33
23.15
13.37
10.35
30.16:
28.56
59.36
37.83
4316
41.19
V.R0C
86.12
71.79
15.00
66.00 69
07.72
73.91
67 55
83.55 68
75.76
61.10
31.75
71.56
72.16
45:36
59:24
61:65
62.00
AE-F
77.59
78.31
73.59
41.39
73.47
75.06
54,00
55.00
MDT
65:44
-6.81
11
38.91
16
37.61
01:1
53.00 18
9153
15.23
15.95
41.00
67
14.54
33.00 km
13
01.01
45.77
V-800
85082
66.75
17.06
A1 00
01.28
54.07
12.30
74.56
15.10
44.55
77
76.56
71 K7
$1.09
00
55.96
$5.30
Table 2: The notation with indicates the results of extending the baselines to their multimodal
versions using the recent time series multimodal framework MM-TSFLib, where both time series and text data from datasets are utilized.
Datasets
Metric
MindIS
DADA"
LMixer*
UTimme
Timer"
UniTS*
G4IS*
Modern"
ToNett
Putch"
TranAD
i'rruss
82.66
69.75 73
76.30
73:91
76.38
76.80
77.05
77.73
75.
Weather
57.48
30:42
44.55
53.42
V-OC
82.64
61.51
75.20
73.71
73.26
75.21
74.61
81.67
52.06
80.08
78.72
70.11
ART
74.37
64.50
61 61.46
65.38
60.36
65.42
67.33
72.13
66.71
66.28
50.53
22:49
Energ
VPPR
50.36
34.38
30.91
30.44
29.57
31.34
31 31.83
37.44
39.8%
34.66
33.74
36.21
V-ROC
74.44
55.63
49.00
49.65
46.0
51.89
53.52
66.37
59.80
58.47
56.38
65 62
AIFFF
85,29
83.84
X3.76
76.73
54.52
X3.43
X4.44
81.36
50.21
81.71
61.38
76.02
Erwirenment
V-PR
56.79
$4.20
$1.22
35.64
$1.30
50.05
56.65
41. 41.36
50.30
45.52
4,93
25 85
VROC
93.78
88.02
91.74
84.20
92.02
91.95
90.22
89.14
14.29
73.66
AF:F
90.28
84.23
77.38
89.61
78.
53.15
44.27
V-OCC
89.36
72.08
75.21
66.96
75.92
74.25
80.43
39.22
78.94
74.50
41.24
77.17
ART
83.89
81.41
78:23
73:20
79.05
78.04
81.37
81.53
$1.92
76.49
68.03
78.23
BWJ
VPPR
50.42
43.18
34.06
25.71
33.36
39.99
43.95
45.41
43.28
36.22
17.87
29.79
V-ROC
84.12
71.06
69.3
67.57
68.00 19.19
74.30
76.91
83.98
75.91
72.21
49.85
74.11
AIFFF
89.19
77.89
75.31
72.33
77.53
76.70
M1.86
81.08
50.62
78.57
63.60
77.47
MTST
V:PR
65.44
47.22
42.61
25.31
38.23
37.78
12.65
45.88
52. 52.30
41.85
14.55
33.58
VROCC
83,02
68.06
61.72
49.16
60.21
58.82
73.39
82.66
73.57
77.72
28.88
69.95
on all datasets, demonstrating the superior ability of MindTS to capture and integrate multimodal semantics. More baselines and metries evaluation results can be found in Appendix C Additional forecasting extension results are provided in Appendix H
4.3 MODEL ANALYSIS
We analyze the effectiveness of fine fine-grained time-text semantic alignment and content condenser reconstruction, and visualize the anomaly scores. We conducted additional analytical experiments, which are presented in Appendix E. G
K
Ablation study. To ascertain the impact of different modules within MindTS, we conduct ablation studies on: (a) remove the exogenous text representation; (b) remove the endogenous text represen- tation; (c) delete the time-text semantic alignment; (d) remove the content condenser mechanism; (e) remove the cross-modal reconstruction module; (f) reverse the order of the alignment and content condenser mechanism. Figure 3 illustrates the distinct contribution of each component.
We make the following observations: of the text representations from the two views leads to a notable performance decline. This indi- cates that integrating the complementary information from endogenous and exogenous texts helps improve the model performance; (c) removing the time-text semantic alignment module leads to a drop, indicating that effective modality alignment is essential for ensuring reliable anomaly detec- tion; (d) removing the content condenser leads to significant performance degradation, likely due to redundant information from text negatively impacting the model; (e) removing cross-modal recon- struction also leads to performance degradation, suggesting that it enhances cross moosl interaction and helps extract time-relevant discriminative features from the text; (f) when the alignment and content condenser order is reversed, the model performance degrades. This may be because filtering
Ours denotes the complete model. (a) and (b) removing either
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Published as a conference paper at ICLR 2026
6.6
6.0
030
MDT
6:75
033
14
383
0.00
6.0
Factor
Factor
Factor
Figure 4: Results of the sensitivity analysis. The vertical coordinate shows the Af-FF score, with higher scores representing better performance. The dark line represents the mean of experiments, and the light area represents the range.
is applied before alignment, causing potentially useful time-relevant information to be discarded prematurely.
Parameter sensitivity. We also study the parameter sensitivity of the MindTS. Figure shows the per- formance under different patch sizes p, time series mask ratios m, and compression strengths μ in the Energy and MDT. As the experimental results show, model performance initially improves and then de- clines as the patch size increases. Note that a small patch size indicates larger memory cost. In our experiments, the patch size is usually set to Fur- thermore, find that maintaining mask ratio near 50% generally leads to better performance. As the mask ratio increases, reconstructing the original time series becomes more challenging, leading to poorer model performance. Besides, further investigate the impact of the compression strength As shown in the results, the model maintains high performance across a broad range of values (0.1 to 0.9), suggest- ing that the content condenser is robust varying compression levels. This stability indicates that MindTS effectively balances semantic preservation and redundancy reduction across different compression strengths.
AfF
Figure 3: Ablation studies for MindTS, with the highest metries highlighted dark- colored bars.
Visual analysis. Jssows how MindTS works by visualizing different datasets. The first row shows the original data distribution along with the ground-truti anomaly positions, and the MindTS anomaly scores in the third row. It can be seen that MindTS can robustly detect anomalies. More detailed visualization cases can be found in Appendixes
Ar
Figure 5: Visualization comparisons of anomaly scores from MindTS for all datasets.

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Published as a conference paper at ICLR 2026
Comparison of different LLMs. To more convincingly demonstrate that the performance improve- ments of our work stem primarily from architectural design rather than reliance on specifie LLMs, conducted experiments with different LLMs. DeepSeek is employed as the default LLM in our model throughout previous experiments. As shown in Table our findings indicate that MindT's maintains stable performance across different LLMs and even achieves competitive results when using BERT. This suggests that the choice of LLMs does not exhibit a sigmificant correlation with MindTS performance, and different LLMS can be flexibly adopted within MindTS.
Table Comparison of different LLMs. Metrics include Aff-F, V-PR, and V-ROC for each dataset.
Mathod
Wiather
Enrrgy
Eavinummntt
KR
EWI
MDT
Menric
ATE-
V.Pg
V.ROC
A/FFF
V-FR
V-ROC
AFFFP
V-P9
V-R0C
AR-F
V-392
V-R0C
AI--F
V-31
V-ROC
AF--F
V-PR
V-RDC
GPT2
92.71
57.81
82,97
73.58
43.78
58.40
$4.05
52.83
93.96
91.21
55.85
50.15
$1.30
48.97
$0.95
65.56
60.24
30.81
50.08
83.62
72.99
43.07
56.00
84.75
01.14
55.25
91.01
8463
43.91
78
87-30
76.37
M.AAA
25.64
49:31
14.38
84.83
02:86
89:17
65.48
31.00
SeepSeck
$2.00
32.64
74.37
74.44
56.79
83.89
$4.12
59.19
65:44
$3.02
Variant of content condenser. To provide more comprehensive analysis, we examine a con- tent condenser variant that explicitly conditions its token retention probabilities on both text and unmasked time patches.
As shown in Table our original design still outperforms the variant in most cases. This is because allowing the content condenser to access unmasked time patches introduces a potential shortcut. While the intention is to provide additional guidance, makes the model focus on the time series modality. This variant tends to identify text that appears superficially aligned with known temporal patterns, rather than selecting text based on its actual semantic contribution to the reconstruction task, which is achieve by the moosl semantic complementarities. As result, the ability of the condenser to filter redundant content may be reduced.
To assess the contribution of the smoothness term LSM, we conduct ablation study. As shown in Table 5 removing LSM leads to a performance drop. This suggests that the absence of the smoothness constraint enforced by LSM may lead the model to generate incoherent and unstable compressed outputs.
Table 4: Performance comparison of content condenser variants conditioned with and without un- masked time patches. The best results are highlighted in bold.
Method
Weather
Knergy
Emvinomment
KR
EWJ
MDT
Mende
AFF
V-PR
V.BCC
AFFF
V.PR
V.80C
ASFF
V.P8.
V-ROC
AFEF
VIRR
V-80C
AFFF
V-F8
VROC
VIR
MBBCC
MindTS
50.36
Table 5: Ablation study on Lsss (all results in %, best results are highlighted in bold).
Method
Weather
Energr
Eastronment
K.
EWJ
MDT
MixeTS owlo Case)
81.59
55.83
$1446
71.26
48.53
73.18
$3.40
54.00
91.58
63.49
46.20
08.31
82.57
49.25
$3.66
36.39
63.23
$1.11
MinfTS
82.66
$7.48
$264
7637
50.6
74.44
85.39
36.79
93.78
99.28
53.15
39366
K3W
91.42
54.12
59.19
65.44
83402
5 CONCLUSION
In this work, we propose a highly capable multimodal time series anomaly detection model MindTS. The MindTS model is designed to address the limitations of existing unimodal approaches by ef- fectively leveraging both time series data and textual information. Overall, it integrates text repre- sentations from both endogenous and exogenous views, enabling a fine-grained understanding of text semantics for precise time-text alignment. In addition, the content condenser filters out re- dundant information. The condensed text is further utilized for cross-modal reconstruction of the time series, optimizing cross-modal interaction. These components collectively empower MindTS with strong anomaly detection capabilities. Comprehensive experiments demonstrate that MindTS achieves competitive superior performance.
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Published as a conference paper at ICLR 2026
ETHICS STATEMENT
Our work exclusively uses publicly available benchmark datasets that contain no personally identi- fiable information. No human subjects are involved in this research.
REPRODUCIBILITY STATEMENT
The performance of MindTS and the datasets used in our work are real, and all experimental results can be reproduced.
ACKNOWLEDGEMENT
This work was partially supported by National Natural Science Foundation of China (62372179).
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THE USE OF LARGE LANGUAGE MODELS (LLMS)
In this work, we only adopt large language models in our methodology and endogenous text genera- tion. Specifically, employ large language models as the text encoder of MindTSS to extract textual features. To generate endogenous text for the multimodal time series corpus, we provide raw time series to the models, encouraging them to produce descriptions of data characteristics. Note that we do not use large language models in writing.
A EXPERIMENTAL DETAILS
A.1 DATASETS
The Table 6 provides summary of the statistics for the publicly available real-world datasets (Liu it al. 2024b; Dong et al. 2024, Miao et al. 2024). To ensure broad coverage and initial relevance of exogenous text to time series, text sources are collected through web search and targeted crawling, combining widely sourced online content with domain-related reports. To ensure semantic relevance between exogenous text and time-series data, 2-3 domain-specific keywords are defined for each dataset and used for web search. For each keyword, the top-ranked search results are collected, and structured information such as timestamp, source, title, and content is extracted. For report- based sources (e.g., governmental or institutional reports), all available documents are parsed, and only the sections whose topics match the corresponding domain characteristics are retained. To prevent future ground-truth trrtt leakage, two safeguards are applied. First, all collected texts contain explicit timestamps, ensuring that no future documents are matched with past observations. Second, each text is further separated into historical factual statements and predictive descriptions, and only factual content is retained. This prevents any predicted future outcomes or implicitly revealed future values from leaking into the model. To comprehensively evaluate the performance of MindTS, we evaluate 6 real-world datasets that cover 4 domains. The anomaly ratio varies from 5.81% % to 17.23%, the range of feature dimensions varies from to 9, and the sequence length varies from 1622 to 15981. Exogenous texts are often collected from diverse sources such as official reports and news articles, which tend to focus on background context or general conditions. We refer to such text as background text, and the majority of the datasets we used also fall into this category. Therefore, our work focuses on this type of text. Temporal alignment is achieved through binary timestamps that mark the start and end dates of each text (Liu et al. 2024b). This provides a feasible and realistic alignment strategy. For example, in the energy dataset, exogenous texts are energy reports from the U.S. Energy Information Administration, which describe contextual factors such as market demand and economic cycles. Similar background-orientee industrial monitoring and other domains. Importantly, our model does not heavily rely on strict temporal alignment. MindTS can operate under window-level matching, making it applicable to practical scenarios where text loosely or sparsely aligned with time series.
alignment is common in
To investigate whether MindTS can be extended to broader multimodal time series tasks, we also evaluate its forecasting performance on three widely used benchmark datasets covering agriculture, climate, and social good Liu et al. (2024b). Detailed statistics are provided in Table 7
Table 6: Statistics and descriptions of datasets used for multimodal time series anomaly detection. AR (%) denotes anomaly ratio.
Dataset
Bim AR (%)
Aug Total Length
Timespan (stari -ndd)
Deseription
Weather
17.10
12330
3012.7 2112.7.17- 2003, 10:20
Temperatun and humiliy staticiss and roports collected isen wibito..
Unergy
17:25
1612
4.5 2504.4 29
Giseline price sunstic and repors collected from the Information Adminsstaiio.
Envimament
15981
1980. 1.1 2023.9.30
Envioamenual Protection Aumuy Fizancia datsects inchak memercal avck des Yahoo
Quility lndes and relued soponts collested
20120-4.1
9.06
2658
2009.1
Fizance and news inforzation collected varions funncill
MDT
11.17
2732
2009.64
200000
news wetsiees NAUMQ Bloomberg and others.
A.2 BASELINES
We extensively compare MindTS against 19 basclines, including (1) LLM-based methods:
LLMMixer (LMixer) (Kowsher et al.) (024). UniTime (UTime) (Liu et al., 2024c). GPT4TS
(G4TS) (Zhou al. 2023), CALF (Liu et al. 2025b); (2) Pre-trained methods: DADA (Shentu
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Table The statistics of evaluation datasets for the forecasting task.
Prediction Length
Avg Total Length
Timespan (start end)
Dataset
Dim
Description
Agriculture
{6. 8, 10, 12]
496
1983-2024
Retail Broiler Composite.
Climate
{6, 10, 12]
496
1983-2024
Drought Level.
SocialGood
{6, 8, 10, 12)
900
1950-2024
Unemployment Rate.
al.
2025), Timer (Liu et al. 2024d). UniTS Gao al. 2024); (3) Deep learning learning-based meth-
ods:
ModernTCN (Modern) (Luo & Wang 2024), TimesNet (TsNett (Wu et al. 2023). DCdetector
(DC)
Yang et al. 2023c), Anomaly Transformer (A.T.) (Xu et al. (021). PatchT'T (Patch) (Nie
et al.
2022). TranAD (Tuli et al.) 2022), DualTF (Nam al. 2024). iTransformer (iTrans) (Liu
ct al.
2023); (4) Non-learning methods: PCA (Shyu et al. 2003), Forest (IF) (Liu et al. 2008
LODA (Pevný 2016), HBOS (Goldstein &
2012).
Incorporates multi-scale decomposition of time series data and leverages pre-trained LLMs process both multi-scale signals and textual prompts, effectively utilizing the semantic knowledge of LLMs for comprehensive temporal analysis.
LLMMixer (LMixer) (Kowsher et al. 2024):
UniTime (UTime) (Liu 2024c): Focuses prompt engineering by introducing learn- tem-
able prompts, prompt pools, and domain- speciiic instructions to elicit poral knowledge from large language models.
GPT4TS (G4TS) (Zhou et al. 2023): Adopts selective fine-tuning of key LLM components such as positional encodings and layer normalization, enabling efficient adaptation time series data while retaining most of the model's pre-trained capabilities.
CALF (Liu et al. 2025b): Proposes a cross-modal fine-tuning framework that mitigates distributional discrepancies between the temporal prediction and the aligned textual source branches, enhancing alignment across modalities.
purpose anomaly detection model for time series, pre-trained on wide range of domains and readily adaptable to various downstream tasks.
DADA (Shentu al. 2025); Develops
Timer (Liu et al. 2024d): Unifies heterogeneous time series into single sequence and
performs predictive anomaly detection using a sequence modeling approach.
UniTS (Gao et al.) 2024):: Transforms multiple tasks into unified token- based representa- tion using prompt-based framework. For anomaly detection, it generates masked tokens and utilizes denoised outputs to identify anomalies.
ModernTN (Modern) (Luo & Wang 2024): Adopts purely convolutional architecture to decouple and model temporal, channel-wise, and variable-wise relationships in multivariate time series.
TimesNet (TsNet) (Wu et al. 2023): Employs modular structure to decompose complex temporal patterns into different frequency components and maps one-dimensional time se- ries into a two-dimensional space to jointly model intra- and inter-period dynamics.
DCdetector (DC) (Yang et al. 2023c): Uses contrastive learning from both patch-wise and point-wise perspectives discriminate between normal and anomalous patterns.
Anomaly Transformer (A.T.) (Xu et al. 202T): Based on the hypothesis that anomalies exhibit stronger associations with nearby time points, it uses a minimax strategy to amplify association differences and enhance anomaly discrimination.
PatchTST (Patch) (Nie et al. 2022): Applies channee-indepeedent patching to multivariate time series, improving the model's 's ability capture localized temporal features.
TranAD (Tuli et al.) 2022):4 A Transformer based Anomaly Detection Model that amplifies reconstruction error through adversarial training.
DualTF Nam al. 2024): Employs a dual-domain architecture with nested sliding win- dows, where outer and inner windows handle time and frequency domains, respectively, aligning their anomaly scores to enhance detection.
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iTransformer (iTrans) Liu et al. 2023): Embeds time information into variable tokens and applies attention mechanisms to model multivariate correlations.
PCA (Shyu et al. 2003): Detects anomalies by measuring the deviation of samples in the
principal component space, assuming anomalies lie far from the normal data distribution.
IForett (IF) (Liu et al. 2008): Detects anomalies by explicitly isolating them through re- cursive partitioning rather than modeling normal behavior.
LODA (Pevný) 2016): Approximates joint distributions using multiple one-dimensional histograms to identify outliers.
HBOS (Goldstein & Dengel 2012): : An unsupervised histogram- based anomaly detection method.
A.3 METRICS
This subsection introduces the metrics used in this study, which are mainly categorized into two
types QQu al. 2025b). The first is label-based metries, including Affiliated Precision (Aff-P),
Affiliated Recall (Aff-R), and Affiliated Fl-score (Aff-F) (Huet et al. 2022), Accuracy (Acc), Pre- cision (P), Recall (R), FI-score (FI), Range Precision (R-P), Range Recall (R-R), and Range F1-
score (R-F) (Tatbul et al. 2018). The second is score-based metrics, including the Area Under the Curve (A-P) (Davis & Goadrich] (006), the Area Under the Receiver Operating Characteristic Curve (A-R) (Fawcett) 2006), the Range Area Under the Precision-Recal Curve (R- A-P), the Range Area Under the Receiver Operating Characteristic Curve (R-A-R) (Paparrizos et al. 2022). the Volume Under the Precision -Reclll Surface (V-PR), and the Volume Under the Receiver Operating Characteristic Surface (V-ROC) (Paparrizos et al. 2022). MindTS evaluates all metrics assess each method's performance.
A.4 IMPLEMENTATION DETAILS
We adhere to the evaluation protocol proposed in TFB (Qiu et al. (0224) during testing by disabling "drop last" operation, ensuring fair comparison across all models. conduct experiments using Pytorch with NVIDIA Tesla-A800-8GGI GPUs. We employ the Adam optimizer (Kingma & Ba 2015) during training.
baselines are based on our runs, using the identical hardware. We employ official or open-souree implementations published on GitHub and follow the configurations recommended in their papers. The initial batch size is 64, which can be halved (down to minimum of 8) if an Out-Of-Memory (OOM) error occurs. We assign equal weights of 1 optimization objec- tives. Experiments show that this sample configuration delivers stable and competitive performance, indicating that additional tuning unnecessary.
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B
LEMMA PROOFS
In this section, we explain the proof of Equation equation g in our paper.
Proof. The definition mutual information described as
P P(Zuext Zooo)
(Znexi Zeonn
ΣΣ
PiZrext: Zeon) log
PP ((zzet) ) ) ZZonn
Zurmi Zec.
(13)
log ie Zeon) TP (eeee (Z
Then, by introducing a variational approximation G( ), we can further derive that
P P(Zzext, Zeom)
Ezeu Zene
log
IP P((Zuxx) )P (o
IP P(Zuoal Zuex))
Ez Zone
log
(14)
P(Zeon)
IP(Zeon Zreat)
(Zenn
Eza,, Zoce
log
+ log
(Zeco)
IP P(ZZoo)
Based on the KL-divergence, the second term of the above equation can be rewritten as
G(Zeon)
PPZoon)) log G(Zcoa)
Ez Zeee Zice
log
Ez Zecn
P(Zeon))
(ZZoo)))
P(Zeoo)
(15)
Ezzen ieeon
IP IP(Zeoo) log
C (Zoon)
-Ezun Zeco
(P (Zeun)
Z
Such that
[ZZeetii Zooa)
EEec (IP((P(Zeoo))) Zuex) ) | (Zeon))) Ezzent Iecco (IP (Zoon))
(16)
< Eizen [KT,(IP (Zeon Zuex) | (Zem)))
It should be noted that G(Zco) is variational approximation, such that the distribution (Zeon) can approximate (Z(ceo)) in the process of optimization, that is G(Zzn)) IP((z.n). In this case, I (Zreti Zeon) EZ... [KL(P(ZL (Zoon Zex)] (Z)cco))]] and the upper bound presented in Lemma tight. The proof is completed.
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C ADDITIONAL EXPERIMENTAL RESULTS
In this section, we present the performance of MindT'S and 19 numerical-only unimodal methods on additional evaluation metrics. Specifically, Tables 13 present the comparative evaluation re- sults across the following metrics: (AUC-ROC, ROC, VUS-ROC), (Accuracy), (AUC-PR, R-AUC-PR, VUS-PR), (Precision, Recall, Fi-score), (Range-Recall, KRane--Precision Range-F1- score), and (Affiliated Seffliated-Precisionn Affiliated-Recall. Affiliated FI-score), respectively.
Tables 14
18 and 19 present the extension of numerical-only unimodal methods into multimodal forms using the MM-TSFLib framework (Liu et al. 2024b), and compare them with MindTS across a comprehensive set of 16 evaluation metrics.
15 I6
Table 8: Average A-R (AUC-ROC), R-A-R (R-AUC-ROC) and V-ROC (VUS-ROC) accuracy for MindTS and all numerical-only unimodal methods. The best results are highlighted in bold, and the second-best results are underlined.
Datasets
Weather
Eastroument
Metiic
33B
R-A-R
V-80C
A-R
R-A-8
V-BOC
A-R
B-A-R
V-BOC
A.R
R.A-R
V.ROC
.-8
R.A.R
V-ROC
A.R.
R.A.R
V.ROC
HBOS
64.47
$4.12
$4.16
60.90
51.05
51.50
55.42
50.70
$1.08
13.16
6:400
61.41
71.82
51.02
62.07
50.25
54.36
55.30
LODA
PCA
67.37
5100
47.51
45.26
44,09
TTans
43:33
34:45
73.37
65.00
64:35
63.06
82.15
30.96
73.51
83.20
34.41
76.13
76.17
58 20
73. 16
79.01
67.65
71.81
DuITF
64.49
28.05
57.84
40.90
38.00
38.35
45.54
31.54
0.30
68.77
55.45
54.51
74,12
50.00
64.31
75.13
60.50
63.35
TonAA)
85,51
70.35
75.15
67.01
56.65
56.37
25.12
9.85
14:30
54.64
$7.02
41.05
60.35
46.
40.00
44.10
24.47
28.55
Purch
80.00
$11.7
79:97
06. 70
01.39
50.31
94.37
91.12
90.86
82.15
73.72
74.55
75.55
04 26
71.56
$4.55
75.61
71.69
31.75
ToNer
81:10
85.11
10.07
$7:07
79.00
75.
77.01
74.56
Modem
90.66
$2.36
81.14
70.90
63.34
65.05
91.53
90.25
99.78
03.99
89.37
99.75
$7.52
$3.72
$3.58
58.77
$1.50
82.30
G47S
24:47
71.4%
70.03
46.95 46.54
$3.54
15.10
75:10
63.10
66.79
18
65.15
07.30
75.45
56 16
63.95
74.75
10.00
67.99
CALE
70.48
64.07
63.63
61.56
59.26
57.35
59.54
41.01
57:35
65.09
43.82
53.22
67.30
55.57
$9.03
53.13
35.41
39.1
92.08
71.32
73.91
UTine
81.09
09.97
91.37
71.49
73.55
67.15
64.00
LMirrr
79.60
72.54
76.91
61.3 31
55.25
$3.04
92.99
$7.83
99.75
65.77
49.01
52.70
$7.60
41.75
46.30
60.30
42.44
47.06
DADA
66.37
61.95
63.05
02.33
55.78
54.37
70.33
57.27
$4.37
79.53
09.91
70.33
79.11
08
71.79
79.04
63.94
66.75
Mied75
84.6
33.80
92.64
$1.36
73.51
74.44
96.33
$4.04
93.78
93.51
83.63
89.86
87.95
53.12
$4.12
90.46
83.15
83.02
Table 9: Average ACC (Accuracy) measures for MindTS and all numerical-only unimodal methods. The best results are highlighted in bold, and the second-bes best results underlined.
Datasets
Weather
Energy
Environment
KR
EWJ
MDT
Metric
ACC
ACC
ACC
ACC
ACC
ACC
HBOS
84,60
$9.69
94.87
95.86
87.03
90.48
LODA
87.60
$2.92
92.03
95.85
86.28
90.48
IF
85.62
55.38
84.87
94.54
86.84
88.10
PCA
62.72
59.08
49.27
82.11
72.18
65.02
iTans
67.02
81.46
77.97
68.70
77.84
DulTF
53.65
23.08
42.89
86 44
85,90
63,92
TranAD
64.67
67.08
29.20
90.21
64.66
41,58
Patch
77.76
49.85
90.06
84.37
85.53
83.88
A.T.
67.10
$7.54
90.37
78. 53
50.56
$7.33
DC
72.85
71.08
54.45
72.32
82.71
74.36
TsNet
81.36
68.00
87.21
$7.95
80.26
86.45
Modern
81.28
$1.38
90.72
89. 00
88.16 16
84.80
G4TS
65.19
39.38
82.37
74.01
79.51
83,70
CALF
63.01
68.31
66.
87.19
76.70
71.25
UniTS
71.80
45.54
90.97
87.38
79.14
85.16
Timer
66.73
44.92
90.03
93.79
81.77
82.42
UTime
81.12
61.23
90.06
91.90
77.26
85.71
LMixer
65.76
45.23
89.97
76.27
71.43
65.57
DADA
58.39
59.38
94.19
92.47
85.71
87.18
MindTS
84.76
80.00
90.09
91.15
88.91
93.96
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Published as a conference paper at ICLR 2026
Table 10: Average A-P (AUC-PR), R-A-P (R-AUC-RR) and V-PR (VUS-PR) accuracy measures for MindTS and all numerical-only unimodal methods. The best results are highlighted in bold, and the second-best results are underlined.
Datasets
Weather
Energy
Envizonmnt
KR
EWJ
MDT
Metric
A-P
R.A.P
V-PR
A.P
R-A-P
V-FR
A-P
R-A-P
V.PR
A-P
R-A-P
V-PR
A-P
B.A.P
V.ER.
A-P
R.A-P
V-PR
HBOS
31.16
46.37
46.55
21.55
42.14
42.57
16.97
29.54
50.30
41.09
51.89
52.06
25.24
38:45
43.19
29.06
45.16
64.77
LODA
41.22
54.75
55.05
20.75
68.94
48.63
9.58
18.19
18:56
40.14
51.31
51.82
24.1
57.46
40.008
29.81
45.18
44.63
IF
35.4 64
49.65
49.65
21:17
15.19
46.03
6:18
8.26
3.94
12.31
43.07
43.31
22.50 26.86
36.74
57.61
22.41
33.65
35. $3
PCA
25.00
47.47
47.1%
21.60
43.80
46
5.67
16.05
17.82
10.18
18.99
20.18
10.99
15:44
19:53
12:10
19:55
22. 21
TTan
41.22
42:08
42.5 42.56
35.63
35.71
35.82
34.90
23.00
24.87
36.05
24.95
20.57
34:42
24.90
28.98
46.01
32.94
36,
DuTTF
25:22
28.05
29.27
22.55
22.84
23.52
5.35
5.47
6.53
21
17:73
17.92
42.45
31.17
53.75
50.5 54.54
31.52
33.83
ThaAD
68.90
$2.04
$2.08
36.35
33.17
33.80
7.09
4.49
4.91
53.23
25.04
29.42
27.85
15.20
17.80
25.60
13.11
14.33
Punch
53.39
49.51
51003
34,25
35.25
34.41
58.92
45.65
45.78
53.60
35.32
56.18
47.91
33.37
56.05
54.11
39.70
41.67
AT.
16.71
18.35
19.17
14,02
19.24
19.69
14.06
16.21
18.34
7.01
6.46
7.94
3.57
0.01
10.85
15.02
13:20
15.93
DC
17.08
18.06
18.35
17.69
21.77
22.57
6.48
6.55
7.59
4.10
7.04
5.09
10.38
12:52
15.37
11.59
13:30
15.71
ToNet
47.63
50.58
50009
32.04
38.17
38.61
64.14
50.62
50.64
67.47
52.83
51,50
54.99
41.84
43.15
55.47
48.60
50.54
Modem
51.98
$2.67
32.13
33.16
35.54
36 26.00
55.34
62,50
42.26
36.95
30.69
39.95
53.36
31.88
55.25
05.00 48
31:22
12.15
GRTS
64.12
41:37
41. 1.330
33.75
31.10
31
35.37
22:14
23:94
36
31.53
58.23
46.75
32.83
35.81
50:40
42.48
48.81
CALF
57:38
34.58
37.07
31.93
33.61
33:49
9.19
7.49
3.96
18
13:25
16.04
12:54
17.53
20.56
16:35
12:56
15.55
UnITS
42:19
44.15
27.51
30.70
11.04
64.13
50.55
50.34
55
40.75
50.15
35.70
51.45
16.14
11.61
Timer
45.87
45.21
35.05
64.52
51.17
51.42
66.72
51.59
51.41
44.01
30.67
55.86
35.35
54.67
37.95
49.57
63.22
36.30
43:43
56.00
LMierr
89.71
32.85
35
50.94
13.25
15.13
18:11
12:36
19.86
25.00
56.20
46.95
63:00
MindTS
53.38
57,85
57.48
50.36
69.45
$6.81
56.79
67.53
52.64
53.15
61.75
49.31
76.51
65.44
Table 11: Average P (Precision), R (Recall) and F1 (Fl-score) accuracy measures for MindTS and all numerical-only unimodal methods. The best results are highlighted in bold, and the second-best results are underlined.
Datasets
Weather
Energy
Environment
KR
KWJ
MIST
Metrie
R
F1
P
R
F1
P
R
F1
R
F1
R
F1
P
F1
HBOS
58.61
33.59
43.94
24:14
62.50
54.83
92.31
12.50
22.64
23.91
51.5
60.71
38.89
$2.83
44.90
64-52
$2.79
43.48
LODA
73.97
42.42
53.0 92.92
22.29
69.64
33.77
26:21
20.43
22.96
76.19
48.48
59.36
36.
65.73
43.41
62.86
36.07
45.83
IF
62.09
40:76
45.21
23:03
64.29
$4.39
8.15
15.59
10.70
56.67
51.52
53.97
37.31
47.17
41.67
45.45
32.79
38. 10
PCA
27.81
73.93
40:41
24.16
96,43
35.12
5.51
47.85
9.85
15.56
42.42
22.76
13.18
32.07
13.58
13.89
40.59
20.75
ITras
30.48
72.51
42.92
22.67
67.86
34,21
19.67
70.96
30.81
18.84
78.79
20.41
22.44
06.04
33.49
30.00
73.33
42.65
DUIFF
17.27
99.53
29:43
17.36
46.43
30,00
5.43
53.5
53.76
9.87
5.82
81.5
10.87
36,90
58.40
45.26
31.50
65.57
42.55
TunAD
31.65
91.34
47.0 47.05
25.24
69.66
32,70
2.20
35.58
4.26
17.48
54.55
26.47
13.02
47.17
20.41
8.65
44.26
14.48
Parch
37.50
71:23
49
21 96
83.91
34.81
34.36
77.96
47.70
34.49
72.73
36.64
36.36
60:38
45.55
30.05
67.31
49.40
A.T.
13.89
17.78
15.50
9.00
17.56
12.50
23.48
70
25.96
7.41
10.
11.00
8.55
43.40
14.50
14.00
45.50
25.46
DC
12.65
9.95
15.38
10.71
12.63
7.07
56.54
12.53
7.46
30.30
11.98
15.65
15.37
17.09
15.04
27.57
19.54
TeNet
66.40
58.00
51.58
21.40
33,95
29.22
$4.41
43.63
47.17
15.76
58.14
37.14
73.58
49.37
43.69
73.77
54.88
Moden
2963
48.17
78.79
44.05
54.01
40.68
53.63
GITS
27.50
72.27
20.70
$3.53
33.22
14.46
69.39
23.96
16.96
$1.82
43.28
54.72
48.33
37.72
49.14
CALF
26.67
13.58
45.45
22.92
$6.60
1868
22.50
UnTS
35.96
50.34
26.05
71.70
44.19
$1.70
Time
$6.00
20:53
$3.53
$1.82
58,34
64.15
41.21
36,00
48.30
49.54
IMber
20.95
27.71
DADA
25.03
66.35
20.10
31.54
50.00
77.96
60.92
37.50
72.72
49.58
38.14
69.81
49.33
44.33
53.70
MiddS
54.77
62.56
58.41
45.16
75.00
56.35
36.21
92.47
52.04
39.40
78.79
52.53
71.70
56.30
69.44
75.19
Table 12: Average R-R (Range-Recal)) R-P (Range-Precision) and R-F (Range-F1-score accuracy measures for MindTS and all numerical-only unimodal methods. The best results are highlighted in bold, and the second-best results are underlined.
Dotasets
Weather
Eaergy
Environment mt
KR
RWJ
MIST
Metric
R.R
R
R.P
R.R
R
R.F
R.
R.P
R.F
R.
B P
R.
R.R
B.
B.F
R.R
R.
R.F
HBOS
22.51
36.88
27.50
57.14
1.51
2.94
10.50
92.11
18.85
45.15
72.55
$7.58
18.18
53.72
55.55
34.62
89.91
49.59)
LODA
27.78
70.30
39.82
67.36
20.64
$1.65
19:39
31.55
24.00
44.44
77.38
56.16
55. 18
47.86
52.52
35.77
86.74
50.63
IF
28.08
10.24
56.02
64.29
5.13
9.50
15:27
10.16
12.30
46. 1.5
53.03
50.47
$1.36
50.52
50.94
33.85
63.75
44.22
PCA
61.31
11.96
30:07
69.71
1.42
2.76
44.68
5.45
9.73
39.30 26
10.51
16.95
34.09
15.5%
21
37.31
16.47
22. 72.55
DEITF
93.99
12.08
21.41
95.00
3.34
11. 19
54.37
4.43
3.19
74.
9:35
16.82
64.00
42:30
50.00
68.85
37.61
48 65
TTam
87.59
23.33
36.70
73:10
19.49
30.74
70.35
19.30
30.31
81 11.48
15.01
25.35
34.55
20:41
32.01
74-62
27.44
46. 19
TranAD
84.84
24.02
42.86
10.90
24.63
4,45
7.54
26
51.36
19.64
22.78
45.54
18.94
26.92
Patch
62.85
34.29
76.67
11.28
19.67
78.77
37.10
50.64
16.30
25.2 26
35,65
65.00
40.67
50.00
66.75
41.78
$1.39
14.72
22.86
5.59
10.00
45.91
5.53
9..67
45.00
16.81
DC
14.97
16.71
16.84
30.09
6.56
10.34
26.54
19.82
69.90
74.05
84.35
40.83
$3.92
79.55
$1.51
71.54
54
Modem
72.13
75.43
33.63
50.94
41.04
$4.53
77.73
70104
16.08
21.00
26.52
23.81
22.5
08.66
35.78
72.31
31.01
14.05
21:55
22.90
UNIS
79.29
30.15
65.00
18.09
36.11
37.00
51.39
30.37
90.91
27.32
66.15
49.01
79.51
25.20
69.71
13.12
21.57
35.27
39,86
54.92
35.56
66.78
70.91
36.96
48.59
73.08
54.14
UTie
66.97
40:58
50.38
59.76
12.61
20.83
85.31
31.25
83.70
47.81
60.96
90.91
34.29
48.
02:88
57.5 23.00
LMiser
70.37
23.56
77.14
12.53
21.99
85.77
45.54
49.89
18.43
51.27
10.52
17.46
$2.69
15.30
23.59
DADA
65.03
20.32
75.00
21.13
32.47
79.15
48.
39.90
49.54
59.61
75.45
41.99
53.95
64:31
50.50
36.77
MindTS
66.32
45.71
54,12
71.19
34,09
46. 10
92.43
35.55
96
51.77
83.95
42.98
56.85
80.91
49.01
61.04
82.88
70.28
76.06
22

---

Published as a conference paper at ICLR 2026
Table 13: Average Aff-P Coetiiated-Precisionn Aff-R (Affiliated-Recall and Aff-F (Affiliated- score) accuracy measures for MindTS and all numerical-only unimodal methods. The best results are highlighted in bold, and the second- best results are underlined.
Datasets
Weather
Energy
Environment
RWJ
Metric
AFFP
AFER
AFFF
AFF-P
A6-F
AFF-P
AF-R
A6-FF
AFF-R
AS--F
AFF-P
AIF-R
AFF-I
AF-F
HBOS
74.47
35.09
47,70
54.00 B2
57.14
55.55
94.43
12:61
22:25
50:3
64.78
80.15
71.03
91: 16
36.30
50.
LODA
R1.00
38.78
76.60
63:45
71.07
60:06
91.26
19.42
41.88
54.50
71.96
79.18
58.89
87.66
PCA
59.14
71.93
64.91
54.8%
60:71
57.65
36.75
08
55.82
61.94
48.41
51,75
53.93
J8:48
51.06
55.95
52.51
54.06
bulTF
55.12
98.43
70.66
53.22
99.39
69. 32
50.04
87.83
53.06
19.01
88.00 62
63.11
76.97
80.47
73.58
76.21
80.53
78,
64.95
95.95
71.81
57.03
95.37
81
62.84
91
14.43
92:25
79.49
66.
94.27
78.27
69
90,65
78,
TrnAD
64.02
92.16
73.97
$1.4
48.05
49.89
46.21
90.38
61.15
75.25
71.37
73.26
56.68
25.57
09.22
51.82
$3.43
63.93
Patch
66.55
98.24
77.17
53.3
89.52
06.83
76.41
86.56
$1.18
74.63
85.04
79.52
74.19
77.53
73.82
75.94
$3.34
79.47
A.T.
51.90
45.75
49.22
50.85
37.59
43.44
72.95
50.59
59.75
64.90
78.3
70.95
53.09
73.89
53.70
61.41
71.30
06.00
DC
66.00
40.02
42,80
66.00
36.57
47.00
$3,20
74
62.24
61.26
61.91
62.57
39.00
48,10
50.82
46.67
47.45
TiNet
72.72
90.35
91.55
53.07
87.26
66.00
70.05
94
50.41
85.06
85.51
$5.79
75.06
29.92
$1.52
76.93
$3.50
50.05
Modera
72,80
91.45
$1.06
60.89
84.47
70.76
77.12
35.45
$1.00
79,60
89.87
$4.42
75.25
85.33
$1.64
75.60
$6.79
50.81
GITS
59:73
92.41
72.56
53.0
98.58
06.37
64.25
82.9
72.41
70.00
92.14
79.56
74,54
79.34
77.10
77.43
$1.50
80.81
CALF
55.89
91.67
69.77
99.11 14
78.18
67.
53.92
92.3 19
67.24
69.25
77.11
72.97
62.31
$3.07
71.21
54.01
68:33
60.33
CaTTS
64.75
92-48
76.17
53.55
78.53
63.70
76.77
90.47
$3.06
76.67
88
$2.24
69.15
88.38
77.61
75.82
75.08
75.45
Tamar
63:70
92.57
75.46
52 94
69.75
60.20
78.76
92
55.05
91.52
87.67
59.55
72.85
34.08
73.06
78.12
79.00
78.51
UTime
69:40
85.12
76.46
52.76
75.12
61.
76.30
87.94
51.71
85.61
88.54
33.53
68.09
91.89
78.23
78.90
73.84
76.28
LMiser
62 36
90.63
73.68
52.52
88.26
65.85
77.91
91 18
14.44
63.24
84.57
72.36
52.91
90.79
66.85
61.40
75.30
67.65
DADA
56.23
89.32
69.01
52. 94
83.61
63.38
83.81
85.46
84.11
86.72
81 36
64.22
77.90
$4.92
61.25
81.81
74.51
77.99
MindTS
26.88
89.37
82.66
71.50
77.47
74.37
77.38
95.01
85.29
85.91
95.24
90.25
77.90
90.74
$3.89
90.80
87.64
89.19
Table 14: Average A-R (AUC-ROC), R-A-R (R-AUC-ROC) and V-ROC (VUS-ROC) accuracy for MindTS and baselines within the MM-TSFLib. The best results are highlighted in bold, and the second- best results are underlined.
Duaando
Winther
Energy
Enrinomment
KB
EWI
MLTT
Mesic
A-B
B.A-R
V.BOC
A-R
R-A-B
V-ROC
3-R
R-A-R
V:80C
A-R
B.N-R
V-3OC
A-R
R.A-R
V-ROC
J.R.
B-A-R
V-BOC
Teuss
00.76
15.71
71.17
70.67
09.95
Pudh
94.17
92.28
12.94
72.21
75.65
17.72
Tohe*
81.38
$3.20
33.06
11.55
91 11
87:30
65.14
35:02
78.04
80.00
75..5
75.00 91
87.05
06.97
71.97
Modem"
$1.07
$1.87
$1.67
T1.61
16.00
66.53
97.20
89.55
10.14
93.66
91.30
89:32
$7.06
K3.90
83.08
88.49
31.95
87.65
G4TS*
78.95
75.57
74.51
56.38
53.91
53.52
04.53
90088
10.22
85.86
73.03
90.43
$3.36
75.52
76.03
94055
71,67
73.39
CAL.F
12:46
88
76.95
64:00
30.65
54.04
UnISS
75.67
71.93
56.87
58.83
76.81
75.02
UTime*
76:71
74.28
23.71
50.32
51.81
49.85
10.12
83.40
6425
76.45
54
65.56
76.07
65.90
67.57
64.95
45.55
49.15
LMiser
82.77
75.73
75.29
62. 14
13
19.00
15 35
92.04
91.3 91.74
$4.00
74. 54
75.31
77.63
07.13
69. 37
70.83
60.31
61:73
DADA*
67.08
62.52
61.51
53.08
57.33
55.63
03.10
87.74
63.02
80.22
71.03
73.06
78.29
67.45
71.06
79.71
63.52
69.05
MadTS
$3.00
82.64
$1.26
96.33
93.51
10.00
89.56
83.19
34.12
90.46
83.15
R3,02
Table 15: Average ACC (Accuracy) measures for MindTS and baselines within the MM-TSFLI The best results are highlighted in bold, and the second- best results are underlined.
Datasets
Weather
Energy
Environment
KR
EWJ
MDT
Metric
ACC
ACC
ACC
ACC
ACC
ACC
iTran**
76.09
63.69
87.31
70.62
74.44
73.44
TranAD+
64.67
70.15
44.73
87.95
64.47
65.38
Patch*
74.59
44.92
90.22
84.37
85.53
81.32
TsNer*
82.01
66.15
93.25
94.35
84.02
76.92
Moder
80,23
62.46
90.50
89.45
87.22
84.62
G
69.89
41.85
93.00
95.10
85,34
91.21
CALF*
64.75
$2.62
84,90
81.36
80.45
79.12
U
70.58
47.69
90,09
89.27
84.21
$6.08
Timer
67,50
45.54
91.43
93.60
81.02
85.16
UTime
68.11
47.69
76.81
87.01
69.74
73.99
LMixer*
68.69
$2.00
90.56
94.92
82.14
88.46
D
61.02
45.54
94.22
92.47
86.84
87.18
MindTS
84.76
80,00
90.09
91.15
88.91
93.96
23

---

Published as a conference paper at ICLR 2026
Table 16: Average A-P (AUC-PR), R-A-P (R-AUC-PR) and V-PR (VUS-PR) accuracy measures for MindTS and baselines within the MM-TSFLib. The best results are highlighted in bold, and the second- best results are underlined.
Dutasets
Weather
Evvirommnt
KR
EWJ
A-P
R-A-P
A-P
V-PR
R-A-P
V-PR
R.A.P
V-28
A-P
R.A.P
A.P
V-PR.
VTrane"
38.56
00:43
40.30
63
36.17
36.21
35.35
23:91
25.85
25.61
28.12
53,63
25.24
64.10
30.26
33.85
1mnAU
60.91
52.04
52.09
36.54
33.13
33.26
7.08
4.51
4.55
53.21
28:07
28.41
27.89
15:25
17.87
26.06
14.55
Pacch
53.69
49:95
50.17
34 66
35.51
34.00
65
58.65
45.40
45.52
53.00
15.57
36.31
47.90
13:45
36.23
54.50
39.92
41.85
TeNa*
48.29
51.00
50.5%
42.47
18.47
15.85
61.87
50.39
50
67.58
53.05
51.71
55.04
41.95
43.28
00.61
41.55
$2. 23.90
Modem
53.07
54.17
$3.42
13.95
$6.64
$7.44
54.31
41.53
41 36
57.71
41.72
40.85
$4,48
44.64
+5.41
65.38
52.11
45.88
G4TS-
49.31
45.70
45.85
33.55
31.23
31.83
69.73
56,82
56.65
72.14
59.99
$7.93
57
42.31
43.93
68.63
$1,69
$2.65
CALP
42.85
41.39
41.43
38.45
35,10
34
43.76
50.42
31.72
51.19
31.22
31.52
$2.08
21.30
23,62
42.75
26.64
25.70
UnilS*
49.50
64.54
48.58
27.55
$1.00
31.34
64.10
50,30
50.06
56.86
41.95
46.21
50.62
17.56
39.90
53.94
36.28
37.
Timer
49:24
41.32
43.36
37 89
28.91
79.57
64.51
50.97
51 20
66.57
51.81
51.56
44.23
10:88
31.96
55.56
17.30
38.21
17Tmme
46.37
43.05
43.19
93.00 63
10.30 92
30.44
48.1 17
$4.55
35.60 64
$7.99
97.70
17.13
53. 10
13.82
25.71
90.63
72.93
25. 51
1Miser*
52.24
45:00
45.94
36, 74
30.50
30.01
64.55 84
$1.34
51 22
68.67
54.04
$2.95
44.75
1188
94.06
60.18
41.05
42.61
DADA
50.22
50.28
30.42
35.30
33.77
34.3 36
70.37
55,99
56 20
63.25
36.59
45.65
55.06
41.34
43.15
63.19
45.13
47.22
MindSS
58.38
57.85
57.48
50.99
50.19
50.36
86
26.81
56.79
07.52
52.64
53.15
61.75
49.31
50.42
76.51
66.20
65.44
Table 17: Average P (Precision), R (Recall) and F1 (F1-score) accuracy measures for MindTS and baselines within the MM-TSFLib. The best results are highlighted in bold, and the second-best results are underlined.
Datasets
Weather
Easrge
Environent
EWJ
Metrie
R
F1
R
F1
P
F1
P
R
F1
R
F1
F1
Muus
36.02
54.50
43.81
25.78
$1.93
35,87
26.19
65.05
37.35
15.64
84.84
26.42
22.15
62.26
32.67
26.
77.05
39,
TazAD=
31.65
91.94
47.08
27.47
43.04
34.01
2.52
22:58
4.54
26.87
54.35
36.00
13.44
47.17
20.92
12:35
34.43
18.18
Paidi
37.70
74.11
50.04
21.40
82.14
33.95
34.84
78.49
48.26
24.49
73.75
36.64
26.36
60.3 38
45.39
33.60
68:55
45. 16
ToNet*
42.77
55.92
51.53
29.55
69.64
41.49
44.79
69:35
54.43
53.19
75.76
62:50
35.45
T3.58
47.85
29.56
77.05
42.73
Modern*
44.58
65
53.17
76.57
78:57
41.00
34.87
75.12
47.22
34.67
78.
48.15
41.26
71.70
$2.78
40.3
$0.13
53.85
G4T8*
$9.67
78.44
4512
20.44
$2.84
$3.74
44-60
84.41
58. 96
57.78
78.70
66.67
37.52
71:20
20.35
59.15
68:55
63.64
CAL
30.14
50.57
43.87
23,66
78.57
36.36
25.04
80.11
38 16
20.54
69.3
31.72
27.43
53.49
57.35
28.00
59.00
38.71
UniTS*
34,95
$3.65
49.30
21.50
76.59
33.59
35.41
85.48
50.08
$4.21
78.79
47.71
35.24
09.81
46.81
41.98
63.90
50.65
Timer"
32.06
$4.83
47.17
19.60
69.64
30.59
39.37
87.63
56.33
49.06
78.79
60.47
29.31
64.15
40.24
40.20
67.21
50.31
UTlma*
32.64
$1.25
46.57
20.00
67.56
30.89
18.97
91:411
31.42
27.50
66.07
38.94
21.58
79.25
34.20
23.18
57.38
33.02
LMiter*
34.54
55.73
19.35
21.59
67.36
32.76
36.88
87.63
$1.91
56.82
15.76
64.95
30.91
64.15
41.72
48.81
67.21
56.55
DADA"
25.09
04,35
36.12
19.50
71.43
31,13
50.17
77.42
60.89
43.60
69.00
33.49
40.45
67.92
50.70
48.94
05.57
53.33
MixITS
54.77
67.56
58.41
45.16
75.00
56.38
36.21
92.47
$2.04
39.40
20.00
52.53
46.34
71.70
56.30
69.44
$1.97
75.19
Table 18: Average R-R CCange-Recally R-P measures for MindTS and basclines within the MM-TSFLib. The best results are highlighted in bold, and the second-best results are underlined.
and R-F (Range-Fl-scorr accuracy
Datasets
Weather
Energy
Emtrrnment
KR
EWJ
MIPT
Metric
R.B
R.P
R.F
R.R
R.P
R.F
B.R
R.P
R.F
R
R.P
R.F
R.R
B.P
R.F
R.R
R.P
R.F
Iuus
$7.06
26,34
37.87
61148
21.28
31.45
63.78
24.31
35.20
$4.20
10.59
18.81
70,00
20.27
51.43
78.46
25,00)
38,60
TanAn*
87.59
22.80 06.86
56.66
39.29
11:04
13.24
21:13
3.54
6.06
50.37
28.75
36.61
51.36
15.69
34.04
36.5%
19.41
25. 35
Paici
76.28
36.40
90.23
73.10
13.12
23.25
70:48
38.28
51.67
76, 30
23.26
35.65
65.00
50.94
40.48
67.50
35.81
46.50
Toñer
68.30
44.11
$3.85
57.86
28.12
32.89
71.63
46.48
56.38
80.00
58.85
67.82
79.55
38.51
51.99
75.38
26.87
38
Modern"
77.18
37.38
50.57
77.57
24.30
37.58
75:37
37.65
50.22
81.23
42.62
55.91
$0.00
47.65
59.57
80.96
41.51
55.05
G4TS*
78.89
24.91
$7.89
72.62
13.80
23.20
83.97
60.23
81,70
64.52
72.87
78.64
44.41
56.76
68.65
61.01
64.61
CALF"
19.52
30.83
72.14
13.79
23.16
79.95
37.02
71.59
19.19
30.35
29.55
59.11
39,60
UniTs*
50.79
27.05
40.53
70.00
18.24
28.94
85.
96
36.04
30.78
85,93
31.94
29.56
78.54
37,93
66.15
41.09
50,69
Tamas
76.94
24.87
10.29
55.7H
37.62
00.50
$7.15
UTime*
19.17
63.10
16.05
25.59
41.15
26.74
57.88
31.99
IMin=*
76.00
39.71
85.77
16.
61
MindTS
45.71
34,09
51.77
49.01
82.88
76.06
Table 19: Average Aff-P (Affiliated- Preectssond....i AffRR (Affiliated- and Aff-F (Affiliated- F1 Fcore) accuracy measures for MindTS and baselines within the MM-TSFLib. The best results are highlighted in bold, and the second-best best results are underlined.
Datasets
Weather
Energy
En-laomment
KR
EWJ
MDTT
Metrie
AlT-R
R
AIEF
AftFF
AfI-R
AdT-F
AfT-R
AII-P
AIE-F
Thuis
65.34
39:01
75.36
61.39
$3.49
68.74
85.01
76.02
15.99
75.39
67.35
93.65
65.66
77.47
TuaAD
96.53
55.65
46.35
30.53
46.37
85.21
61.3%
72.07
72.50
56.74
63.12
58.33
67.35
63.60
Peich
$7.79
76.83
81.71
65.00
74.07
73.28
22.96
88.77
90.95
78.96
80:21
84.73
70
80.62
Madana
71.03
94.02
$1.50
56.27
81.85
75.68
64TS*
65.85
92.12
76
$2.65
90.78
84.44
85.05
76.80
$6.52
86.29
CATFF
96.23
69:62
UnITS-
92.02
76.55
83.43
36
$3.24
70.00
84.52
87.67
73.11
79.84
UTime*
93.03
65.38
76.75
84.40
92.98
72.53
82.57
68
84:87
39.37
77.47
74.37
77.38
85.29
90.80
$9.19
24

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Published as a conference paper at ICLR 2026
D VISUALIZATION CASE STUDIES
To enable intuitive performance comparison, we conduct a comparative visualization of anomaly scores between MindTS and GPT4TS (the original model and within the MM-TSFLib), shown in Figure MindTS exhibits the most distinguishable anomaly scores compared to existing methods.
Figure 6: Visualization comparisons of anomaly scores between MindTS and GPT4TS for all datasets.
E ADDITIONAL MODEL ANALYSIS
E. CROSS-VIEW TEXT FUSION ANALYSES
In this paper, we use the endogenous text H as the query and the exogenous text H as the key and value to obtain fused text representation Zuext to enhance semantic consistency with the time series and extract the most relevant background information. We further conduct additional exper- iments comparing different attention strategies to demonstrate the effectiveness of this design: (a) MindTS (g/kv reverse), setting H query and H as key/value; (b) MindTS (self-attention), us- ing self-attention only; (c) MindTS (two-way), replace the one-way cross-attention with a two-way block where exogenous text also queries endogenous features; and (d) MindTS, setting H as query and HC as key/value. As shown in Table 20) the configuration where H is used as the query and H as the key/value yields the best performance. In cross-view attention, using H as query and H as key/value allows the model to better extract supplementary information from exogenous that is most relevant each time patch. By contrast, relying solely on self-attention limits the model's ss ability to directly learn interactions between different text modalities. Although two-way cross-attention design possesses a certain level of representational capacity, it does not bring significant performance improvements on most datasets.
Table 20: Evaluation of cross-view text fusion variants.
Fnergy
Mutric
AfE-2
V-tt
V-002
V-BUC
ABF-
V-SEE
V402C
AIH-I
V-HDC:
ABF-F"
V-15E
A35-1
N-PR
V-BDCC
MisdTS oylu revens)
$2.29
57.33
82.27
73.78
48.22
73.06
$3.34
$5.40
92.64
14.14
50.72
58.72
82.71
40.09
84.34
$7.95
65.07
$2.30
NindTS
75.43
82.76
44.64
75.23
MirfIT
$2.66
57.48
33.64
16.37
30.66
76.4
55.39
$6.79
93.6
WL3K
53.15
19
83.99
90.42
$4.13
89.09
65.44
K382
Comparison with LLM condenser. To further illustrate the effectiveness of the content condenser, We add comparative experiments among the following four model variants: (a) w/o text, input includes only time series (no text); (b) w/o filtering, input includes time series and text, where
25

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Published as a conference paper at ICLR 2026
the text is used without any redundancy filtering; (c) LLM-based compression, input includes time series and text, where the text is processed by LLM-based compression for redundancy filtering (the content condenser is removed); (d)conntntt condenser, input includes time series and text, where the text is processed by our proposed content condenser for redundancy filtering.
As shown in Table 21 variant (d) achieves the best performance across all evaluation metrics. Vari- ant (b) performs the worst, even lower than (a), indicating that unfiltered text introduces redun- dancy that degrades performance. This confirms the existence of text redundancy. Variant (c) with LLM-based compression to filter the text achieves better results than (a) and (b), demonstrating that compression helps alleviate redundancy to some extent. Most importantly, variant (d) significantly outperforms the LLM--bsed compression approach, highlighting the effectiveness of our proposed content condenser. Unlike L.LMs, our module explicitly optimized under the multimodal objec- tive to preserve time-aligned semantics and suppress irrelevant textual content, thereby enhancing outlier detection performance. In contrast, LLMM-bssed compression considers text-only semantics, which fails to capture time-aligned semantics.
Table 21: Ablation on text redundancy filtering strategies. The best results are highlighted in bold.
Datasets
(a) w/o text
(b) w/o filtering
LLM-based compression
(d) content condenser
Metric
Aff-F
V-PR
V-ROC
Aff-F
V-PR
V-ROC
Aff-F
V-PR
V-ROC
Aft-F
V-PR
V-ROC
KR
84.11
45.11
79.43
80.52
37.82
74.38
87.32
47.95
88.52
90.28
53.15
89,86
EWJ
81.87
45.22
79.32
78.79
38.47
80.03
80.26
42.90
78.84
83.89
50,42
84.12
MDT
84.00
58.32
81.68
81.79
51.40
75.43
84.31
58.74
81.13
89.19
65.44
83.02
E.2 MULTIMODAT. ANALYSTS
In Table 22 we observe that compared to time series unimodal settings, incorporating time-text datasets (e.g., Energy and
multimodal settings consistently yields better results. Notably, MDT), Mind'TS outperforms the baselines. As reported in Table of the paper, these datasets are relatively small in size but have high anomaly ratios, making anomalies more densely distributed and easier to detect. For models with strong reconstruction capacity, this setting increases the risk of overfitting, as anomalies may also be reconstructed too well, thereby degrading detection perfor- mance. In contrast, simpler methods are less prone to reconstructing anomalies, which sometimes results in competitive outcomes. Nevertheless, across all datasets, MindTS consistently demon- strates superior performance over unimodal settings, effectively integrating multimodal information to enhance anomaly detection.
Table 22: The results of MindTS and (indTo() across all datasets (all results in %, best results are highlighted in bold).
Method
Wenther
Energy
Eurinsnncat
Metric
V-R0C
V.ROC
V.PR
V V-ROC
ATTFF
V.PR
V-BOC
V-PR
V.8OC
V.BR
V/80C
75.36
45.00
74.19
13.14
45.65
71.36
51.16
44.28
56.43
54.11
45.31
79.43
81.87
45.22
75.32
$4.00
55.32
$1.65
DADM
69.01
30.00
61.03
64.38
34.15
54.57
S4.11
$4.20
37.09
54.22
45.90
20:32
81.26
43.36
71.75
77.59
46.51
06.76
ModemiCN
$1.00
65.05
81.53
$3:35
$1.10
MinfTS
12.64
85.29
$6.79
98.25
53.15
W9366
83.89
84.13
83,92
E.3 INFERENCE TIME
In Table we compare MindTS with other models across different datasets in terms of inference time and memory cost. Overall, MindTS achieves competitive inference time while maintaining su- perior detection performance. Regarding memory usage, the additional cost moderate and remains well within the capacity of modern hardware, making MindTS practical for real-world deployment.
E.4 EXOGENOUS TEXT QUALITY ANALYSIS
we compare different types of exogenous text quality variations to eval- uate the robustness of our model: (a) noisy, by introducing random spelling emros within sentences; (b) irrelevant, by replacing the original sentences with unrelated text from different domains; and (c) incomplete, by removing portions of the text descriptions.
From Table 24 to Table 26
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Published as a conference paper at ICLR 2026
Table 23: Run times and memory costs on different datasets. Lower values represent better perfor- mance. The notation with * denotes results obtained by extending the baselines using the recent time-series multimodal framework MM-TSFLib.
Method
Inference Time (8)
Memory Cost (GB)
MDT
KR
EWJ
MDT
KR
EWJ
MindTS
0.2302
0.1977
0,4130
14.69
14.62
14,41
ModemiTIN "
0.1582
0.1383
0.3965
13.61
13.66
13,57
LLMMixer*
0.2585
0.2104
0.4619
14.13
14.08
13,97
UniTime
As shown in Table 24 under single-type settings, the performance of MindTS only slightly de- creases compared to the clean setting. As shown in Table 25] when the noise strength is within reasonable range (e.g.. 0.2 and 0.4), the content condenser effectively filters redundant text information, thereby mitigating its impact. As result, MindTS still maintains robust performance. Under more challenging conditions, such multiple text types combinations (Table 26) or high noise intensity (e.g., 0.8), the performance degradation becomes more noticeable. Nevertheless, the overall results remain within acceptable bounds. Additionally, we clarify that the exogenous texts in our datasets are collected from real-world sources (e.g., news, public reports), which inevitably contain redundant or partially irrelevant content. Nevertheless, MindTS consistently achieves strong results across multiple realistic datasets, demonstrating its robustness and adaptability to real-world text quality variation.
Table 24: Results under different types of exogenous text quality variations (all results in %, best results are highlighted in bold).
Method
MindTS
MindTS (noisy)
MindTS (irrelevant)
MindTTS (incomplete)
Metric
Aff-F
V-PR
V-ROC
AII-F
V-PR
V-ROC
Aff-F
V-PR
V-ROC
Aft-F
V-PR
V-ROC
MDT
89.19
65.44
83.02
87.45
62.12
81.02
86.54
60.10
80.17
87,85
61.95
80.59
Energy
74.37
50.36
74.44
72.53
47.42
72.16
72.21
46.52
71.29
73.13
47.62
73.50
Table 25: Results of the noisy method under different noise strengths (s).
MindTT
Metric
AT-F
V-PR
V-ROC
AI-F
V-PR
V-ROC
AF-F
V-PR
V-ROC
AIFF
V-PR
V-ROC
AI-F
V-PR
V-ROC
MDT
89.19
68.44
83.2
87.45
62.12
81.02
86.38
60.03
79.46
83.71
58.71
78.69
79.84
55.21
76.46
Energy
74.37
74.44
72.53
49.42
73.16
71.17
48.26
70.3
69.35
47.22
69.97
45.02
66 13
Table 26: Results under combined types of exogenous text quality variations.
MindTS
MindTS
Method
MindTS
(noisy irrelevant)
(moisy incomplete)
firrelevant incomplete)
(three types)
Metric
AIT-F
V-PR
V-ROC
AП-F
V-PR
V-ROC
AT-FF
V-PR
V-ROC
AIE-F
V-PR
V-ROC
AIT-F
V-PR
V-ROC
75.28
Energy
74.37
50.36
74.44
70.33
44.71
20.18
71.89
45.36
69.95
69.74
45.26
70.51
66.05
42.08
66.88
27

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Published as a conference paper at ICLR 2026
F ALIGNMENT VISUALIZATION ANALYSIS
In this section, we visualize the learned similarity matrix between time series and text representa- tions before and after alignment (see Figure Before alignment, the similarity distribution appears scattered, indicating weak semantic correspondence between modalities. After alignment, the simi- larity becomes more concentrated along the diagonal, revealing clear associations between relevant time series and text representations. This demonstrates that the proposed alignment module success- fully establishes cross cross-modie modal consistency, thereby enhancing the model's 's ability to utilize textual information.
Figure 7: Visualization comparisons between before and after alignment. The x-axis denotes time representations of each patch, while the y-axis represents text representations of each patch.
In addition, we present qualitative visualizations that illustrate how alignment improves anomaly detection decisions. Specifically, we compare the anomaly score with and without alignment, as shown in Figure 8 The first row shows the original data with ground-truth anomaly positions, the second row displays the anomaly scores the model with multimodal alignment, and the third row presents the anomaly scores of the model without multimodal alignment. When alignment is applied, the model exhibits more distinguishable anomaly scores around true abnormal regions. In contrast, the model without alignment fails to effectively increase the gap between the anomaly scores normal points and anomalies, leading to many false positives.
Figure 8: Visualization comparisons of the anomaly score between with and without alignment.
G PROMPT DESIGN ANALYSIS
The endogenous prompt MindTS is constructed using the template shown Figure 9 Inspired by the analyses in TimeL.M Jin et al. (2023) and HiTime Tao et al. (2024), we explore how different prompt designs affect model performance as follows:
(1) Template variant (MindTS-T), prompts generated using different template formulation, as shown in Figure 10:
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Published as a conference paper at ICLR 2026
(2) Statistical variant (MindTS-SS removing parts of the statistical descriptors: (a) keeping only dataset description, (b) removing min, max, median values, (c) removing trend information, (d) removing lag in- formation;
[Start prompt]
[Datoset description]: The weather doto includes temperature and humidity statistics as well as reports collected from government websites.
[Task description): Reconstruct the lseq_lero steps Aaa the previous <seq_lem steps.
(3) Temporal granularity variant (MindTS-TGi
changing endogenous text generation from the per- patch level to the per-sample level.
[Input statistics]:
Minimum value: <min value>
Maximum value! <mox_value>
As shown in Table 27 altering only the template for-
Median value: <medion__vluee value>
Trend of input:
mulation while keeping the content unchanged has
Top k logs: dlgs>
almost no impact on performance. When few sta- tistical descriptors are removed, the performance de- cline minor. However, when most of the statistics omitted (MindTS-S(a)), performance drops more noticeably, the generated endogenous text becomes too sparse to convey meaningful information. Changing the temporal granularity of en- dogenous prompt generation also leads to noticeable performance differences, primarily because coarse- coarse-grained endogenous text weakens MindTS's ability to achieve fine- fine-graing rree-graied alignment. These results together confirm MindTS's robustness to reasonable variations in prompt design. We clarify that the selected statistical descriptors represent fundamental characteristics of time series. There- fore, the performance improvement does not depend carefully tuning the prompts but rather arises from the intrinsic capabilities of the proposed model.
(End prompt]
Figure 9: Prompt example.
Table 27: Ablation on endogenous prompt design across different variants.
Dataset
Metric
MindTS
MindTS-S(a))
MindTS-S(b)
MindTS-S(e)
MindTS-S(d)
MindTTST
Min
Afl-F
89.19
83.22
87.59
87.62
87.14
88.38
84.78
V.PR
64.05
64.49
V-ROC
83.02
78.72
82.75
82.13
82.90
83.19
80.75
AFF-F
74.37
66.65
73.74
74.03
73.85
74.21
69.31
Energy
V-PR
50.36
44.29
48.77
49.25
49,34
45.32
V VERC
74.44
68.59
72.33
71.78
72.66
72.14
67.97
H TIME SERIES FORECASTING EXPERIMENTAL RESULTS
Although MindTSS is primarily designed for anomaly detection, its architectural components are inher- ently extensible. MindTS proposes a fine-grained time-text semantic alignment mechanism consisting of endogenous text generation, cross-view text fu- sion, and a multimodal aligmment strategy to ensure that time series and text semantics consistently matched. Since accurate alignment is crucial for multimodal tasks involving heterogeneous semantic spaces, the alignment mechanism in MindTS pos- sesses extensibility. Moreover, MindTS incorporates a content condenser to filter redundant textual information before cross-modal interaction. Redun- dant text is common challenge in many multimodal applications. Together, these components make MindTS applicable to other multimodal time series applications.
[Dataset]: :: and humidity statistics as well as reports collected from government websites. [Task]: Reconstruct the <seq_ Jen steps given the previous <seq_ Jen steps. [Input statistics]: Input volue ranges from mmao, with median of <median and an overall <downwards trend, and top k logs are <logs.
The weather data includes temperature
Figure 10: Different template formulation.
To further evaluate extensibility, MindTSS adapted to time series forecasting and compared with forecasting-orientel baselines (L.i et al. (025b) Liu et al. (024b). Beyond anomaly detection, time
series forecasting (Tian et al.) [2255] 2026 Fang et al.| 2026 Mei et al. 2025 Pan et al.) 2023 Feng
al.) Liu et al.] 2025a Li et al. 2025a Cheng al. 2023; Wu et al. 2024) is another critical task in temporal data analysis. As shown in Table 28 MindTS achieves competitive performance across multiple forecasting datasets, demonstrating that the components generalize effectively beyond anomaly detection. These results confirm the extensibility of MindTS to other multimodal time series applications.
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Published as a conference paper at ICLR 2026
Table 28: Multimodel time series forecasting results with forecasting horizons F {6, 8, 10, 12).
Models Metries
MindTT
Time-MMD
Ta TS
0.167
0.269
0. 0.146
0.263
0.140
0.251
8
0.195
0.283
0.189
0.310
0.187
0.282
Agriculture
10
0.228
0.316
0.254
0.320
0.244
0.320
12
0.258
0.343
0.338
0.369
0,290
0.350
AVG
0.212
0,303
0.232
0.316
0.215
0.301
6
0.157
0.225
0.162
0.242
0.174
0.239
8
0,176
0.251
0.168
0.228
0.178
0.242
Traffic
0.167
0.213
0.178
0.237
12
0.181
0.237
0.188
0.246
0.189
0.242
AVG
0.170
0.232
0.174
0.239
0.179
0.238
0.172
0.331
0.199
0.350
0.196
0.350
0.215
0.370
0.216
0.367
0.214
0.376
Economy
10
0.215
0,363
0.224
0.373
0.223
0.367
12
0.242
0.379
0.239
0.388
0.239
0.388
AVG
0.211
0.361
0.219
0.370
0.215
0.368
I ANALYSIS OF ENDOGENOUS TEXT GENERATION
To assess the actual benefit of the endogenous text generation step. we conducted an ablation study in which the model directly uses Hime as the query to fuse exogenous text, without endogenous text generation. As shown in Table 29 the model incorporating endogenous text achieves clearly better performance. The endogenous text is derived directly from the time series, capturing temporal char- acteristics that align closely with local patterns. Consequently, fusing endogenous and exogenous texts enables the model to extract supplementary information from exogenous sources that is most relevant to each time patch.
In contrast, directly using Hime as the query to fuse exogenous text does not yield performance gains. This is likely because the time-series and text modalities inherently reside in different mantic spaces; interacting them directly without prior alignment results in insufficient information extraction due modality discrepancies.
Table 29: Ablation study on H as the query to fuse exogenous text, without endogenous text generation (all results %, best results are highlighted in bold).
Methed
Weather
Energy
Envrenment
KB
FWJ
METT
Metric
AlFFP
Ves
V-KC
AEF
VIR
V-R0C
AFFF
V-HOC
AIFFF
VIR
V-HOC
AIEF
VI2
V-ROC
AIFF
V-RR
V-BOC K366
georyy
MrdTTs
82.66
57.48
82.64
14.77
50.36
74.44
88.29
$6.79
98.3%
90.28
53.15
89.6
809
84.12
89.19
65.44
J ANALYSIS OF MULTIMODAL ALIGNMENT AS A STANDALONE OBJECTIVE
In this section, we compare multimodal alignment trained as a standalone objective with the auxiliary aligmment setting used in MindTS. As shown in Table 30 training alignment as standalone objective does not lead to performance improve- ment. We clarify that although optimizing multimodal align- ment alone may strengthen the alignment depth, it neglects the specific role of the learned representations in anomaly de- tection. contrast, jointly optimizing alignment with other objectives allows multiple losses to guide and regularize each other, enabling the model to emphasize features that are both semantically aligned and relk-nelevant.
Tlme (5)
E/W
MDD
20%
40%
60%%
80%
100%
Data Size
Figure 11: Scalability comparison under different data sizes.
30

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Published as a conference paper at ICLR 2026
Table 30: Multimodal alignment as standalone objective (all results in %, best in Bold).
Datasets
MindTS (auxiliary)
MindTS (standalone)
Metric
Af--F
V-PR
V-ROC
Aff-F
V-PR
V-ROC
MDT
89.19
65.44
83.02
80.33
57.18
76.35
Energy
74.37
50.36
74.44
70.28
44.89
69.61
K SCALABILITY STUDIES
We would like to clarify that due to the current difficulty in obtaining larger-scale multimodal datasets, we evaluate the scalability of MindTS by varying the proportion training data (20%, 40%, 60%, 80%, 100%) the available datasets. As shown in Figure [1] the running time in- creases approximately sub-linearly with the data size. As the data volume grows, total training time increases because more iterations required to process larger datasets. Compared with baselines, MindTS has certain advantage training time (see Table 31). These observations collectively demonstrate that MindTS maintains reasonable computational scalability and remains practical for real-world multimodal time series applications.
Table 31: Training time (s) comparison with baselines.
Datasets
MindTS
GPT4TS*
LLMMixer*
ModernTCN*
UniTime"
KR
80.83
81.17
84.35
19.72
77.98
EWJ
75.56
73.66
70.09
18.65
70.23
MDT
73.76
74.49
70.01
18.61
68.53
31


Reviews:
[{"review_id": "i1qm0dNYnB", "reviewer": "Reviewer_zQng", "summary": "This paper introduces model for multimodal time series anomaly detection that integrates both numerical and textual modalities. MindTS seeks to capture richer contextual and semantic information by aligning time series data with exogenous (external) and endogenous textual descriptions. The method consists of two key innovations: (1) Fine-grained Time-Text Semantic Alignment, which uses cross-view fusion and contrastive learning to ensure semantic consistency between time series and text; and (2) Content Condenser Reconstruction, which filters redundant textual information using mutual information minimization and reconstructs masked time series data to enhance cross-modal interaction. Experiments on six real-world datasets demonstrate that MindTS outperforms or matches SOTA methods across multiple evaluation metrics.", "questions": "1. How is exogenous text collected or preprocessed for each dataset, and what ensures its semantic relevance to time segments? How do you make sure the text doesn't leak future ground truth information?\n2. How does MindTS perform when textual data are missing or random or very noisy?\n3. Could the authors provide runtime or complexity comparisons with other multimodal baselines?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Addresses a novel and underexplored problem: multimodal time series anomaly detection.\n2. Introduces a well-motivated novel dual-text approach  for semantic alignment.\n3. Extensive experiments with multiple datasets and a strong set of 17+ baselines.\n4. Comprehensive ablation and sensitivity analyses that validate the contribution of each component.", "weaknesses": "1. Dependence on the quality and availability of exogenous text may reduce generalizability to domains lacking rich textual context.\n2. The discussion could benefit from more qualitative examples showing how textual alignment improves anomaly detection decisions.\n3. Computational cost and inference time are not reported.", "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": "kCWcLXvlnp", "reviewer": "Reviewer_v5hT", "summary": "This paper introduces MindTS, a multimodal time-series anomaly detection framework that integrates numerical signals with textual information from both endogenous (LLM-generated from time segments) and exogenous (external documents or reports) sources. The model addresses two main challenges: achieving semantic alignment between heterogeneous modalities and filtering redundant textual content. It proposes a Fine-grained Time-Text Semantic Alignment module for cross-view fusion and alignment, and a Content Condenser Reconstruction mechanism that minimizes mutual information to distill informative text for cross-modal reconstruction.\nExperiments on six real-world multimodal datasets show that MindTS consistently outperforms baselines, demonstrating the effectiveness and robustness of the proposed approach for multimodal anomaly detection.", "questions": "1. More details on how the endogenous prompts are constructed and how sensitive the model is to their design would help strengthen the paper’s clarity and reproducibility.\n2. Could the authors elaborate on the extensibility of MindTS? In particular, how might the proposed framework generalize to other multimodal time-series applications beyond anomaly detection?\n3. Would it be possible to include an experiment that skips endogenous text generation and directly uses $H_{time}$ as the query for fusing exogenous text? This would clarify the actual benefit of the endogenous text generation step.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper proposes a well-designed dual-view text alignment strategy that fuses endogenous and exogenous text to achieve precise semantic alignment between time and text modalities, effectively addressing heterogeneous data alignment issues.\n\n2. The proposed Content Condenser, inspired by the Information Bottleneck principle, provides a principled solution to mitigate textual redundancy and noise in multimodal fusion, improving the efficiency of cross-modal interaction.\n\n3. The cross-modal reconstruction task that reconstructs masked time series from condensed text is particularly innovative, encouraging the model to capture truly time-relevant textual information and enabling deeper cross-modal interaction.\n\n4. The paper is clearly written and logically organized, with a coherent flow that makes the motivation well understood.", "weaknesses": "1. The paper claims to be multimodal, mentioning images and videos, but the proposed MindTS framework is actually limited to time-series–text fusion. Its core mechanisms are difficult to extend to other modalities. The authors should clarify this scope in the paper.\n2. The paper lacks sufficient ablation on the design of endogenous prompts. Since the construction of these prompts (e.g., template formulation, choice of statistical descriptors, and temporal granularity) may strongly affect model performance, more experiments would help clarify their robustness and justify the design choices.", "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": "PVqY8aLwYc", "reviewer": "Reviewer_BUH8", "summary": "The paper proposes MSH-LLM (Multi-Scale Hypergraph aligned Large Language Models), which aims to address the problem of multi-scale semantic misalignment when applying large language models (LLMs) to time series analysis. The framework combines multi-scale temporal and textual representations through several well-designed modules, including a multi-scale extraction unit, a learnable hyperedge mechanism for cross-scale interactions, a cross-modal alignment module based on multi-head attention, and a hybrid prompting strategy that enhances the LLM’s temporal reasoning ability. I think the design appears technically sound and could potentially inspire further research on cross-modal temporal understanding with LLMs.", "questions": "1. What is the justification for using a plain average in the hyperedge feature computation? Why not adopt attention-weighted aggregation or other pooling strategies?\n2. The paper claims that the hypergraph structure is learned in a data-driven manner, but it does not provide evidence of convergence or stability. Could you report how the incidence matrix $H^s$ evolves during training, such as changes in sparsity or spectral distribution? It would also be useful to provide a theoretical discussion linking the sparsity of $H^s$ to the model’s generalization ability.\n3. Do affective prompts contribute differently across tasks? For example, are they more effective in zero-shot transfer scenarios such as M4 to M3 compared to few-shot classification? A targeted ablation that removes only the affective prompt could help clarify its role in performance.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper identifies multi-scale semantic discrepancy as a key blind spot in existing LLM-for-time-series (LLM4TS) approaches. The proposed hyperedge mechanism effectively models group-level interactions through learnable hyperedges, which I think could reduce noise compared to traditional patch-based methods such as PatchTST.\n2. The experimental validation appears thorough and convincing.\n3. The framework demonstrates compatibility with advanced LLMs such as LLaMA-3.1-8B and Qwen2.5-7B, which should provide practical flexibility for different application scenarios.", "weaknesses": "1. The hyperedging mechanism lacks interpretability. Although experiments show it improves semantic density, it remains unclear how specific hyperedges correspond to concrete multi-scale temporal patterns (e.g., daily vs. weekly cycles). The choice of the Top-K threshold (η = 4) also seems purely empirical, with limited discussion on why this value is optimal or how sensitive performance is to η.\n2. The necessity of some MoP components is uncertain. The emotional manipulation prompts (e.g., “That’s really important for me”) are not individually validated, so their contribution is ambiguous. Likewise, the benefit of data-correlated prompts such as statistical summaries (max/min) is not analyzed across different task types, making it unclear whether these prompts provide consistent gains.", "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": "qADGsvHorq", "reviewer": "Reviewer_2Z5d", "summary": "This paper presents MindTS, a novel framework for multimodal time series anomaly detection that effectively integrates numerical and textual data. Traditional methods rely solely on unimodal numerical signals, neglecting the complementary semantic information in text. MindTS addresses two main challenges: achieving semantic alignment between heterogeneous modalities and filtering redundant textual content to enhance cross-modal interaction. To this end, the model introduces a Fine-grained Time–Text Semantic Alignment module, which fuses endogenous and exogenous text through multi-view integration to ensure consistent semantic representation, and a Content Condenser Reconstruction mechanism that removes redundant information and enables cross-modal reconstruction for improved interaction. Extensive experiments on six real-world multimodal datasets demonstrate that MindTS consistently achieves competitive or superior performance over existing methods, validating its capability to accurately capture semantic dependencies between modalities and detect anomalies more effectively than prior unimodal or naïve multimodal approaches.", "questions": "See weakness", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces a well-designed multimodal framework that effectively integrates both time-series and textual information for anomaly detection. Its dual-perspective modeling—using endogenous and exogenous texts—captures complementary semantics that unimodal models overlook. This fine-grained alignment significantly enhances contextual understanding, allowing the model to detect anomalies grounded in both temporal dynamics and semantic context.\n2. The proposed Content Condenser Reconstruction module is an innovative mechanism for filtering redundant textual information while preserving meaningful semantics. By minimizing mutual information and enabling cross-modal reconstruction, it not only improves model interpretability but also strengthens the interaction between modalities. This leads to more compact, semantically relevant representations and improved robustness against noisy or irrelevant text inputs.\n3. Extensive experiments on six real-world multimodal datasets validate the generality and effectiveness of MindTS. The model consistently achieves state-of-the-art or highly competitive results across multiple evaluation metrics, demonstrating its robustness, scalability, and strong cross-domain applicability. The paper’s comprehensive comparisons and ablation studies provide clear empirical evidence supporting the model’s advantages and technical soundness.", "weaknesses": "1. The paper lacks a detailed exploration of computational efficiency and scalability. While MindTS integrates multiple components, such as fine-grained semantic alignment and content condensation, the training cost and inference latency on large-scale or streaming time series remain unclear. Without such analysis, its practicality in real-time or resource-constrained applications may be limited.\n\n2. Although MindTS achieves impressive performance, its interpretability could be improved. The model’s multiple fusion and reconstruction layers make it challenging to understand how specific textual cues influence anomaly decisions. More qualitative case studies or explainable AI techniques would help clarify how the semantic alignment contributes to detection outcomes.\n\n3. The experiments, while comprehensive, are primarily limited to datasets with well-structured external text. The model’s robustness against noisy, incomplete, or domain-shifted text sources remains underexplored. This limits confidence in its generalization to real-world multimodal environments where textual information is often inconsistent or less reliable.", "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)