Multi-Scale Hypergraph Meets LLMs: Aligning Large Language Models for Time Series Analysis
Authors: Zongjiang Shang
Dongliang Cui
Binqing Wu
Ling Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Time series forecasting
large language models
multi-scale modeling
hypergraph neural network
hypergraph learning
transformer
Primary area: learning on time series and dynamical systems

Abstract: Recently, there has been great success in leveraging pre-trained large language models (LLMs) for time series analysis. The core idea lies in effectively aligning the modality between natural language and time series. However, the multi-scale structures of natural language and time series have not been fully considered, resulting in insufficient utilization of LLMs capabilities. To this end, we propose MSH-LLM, a Multi-Scale Hypergraph method that aligns Large Language Models for time series analysis. Specifically, a hyperedging mechanism is designed to enhance the multi-scale semantic information of time series semantic space. Then, a cross-modality alignment (CMA) module is introduced to align the modality between natural language and time series at different scales. In addition, a mixture of prompts (MoP) mechanism is introduced to provide contextual information and enhance the ability of LLMs to understand the multi-scale temporal patterns of time series. Experimental results on 27 real-world datasets across 5 different applications demonstrate that MSH-LLM achieves the state-of-the-art results.

Published as a conference paper at ICLR 2026
MULTI-SCALE HYPERGRAPH MEETS LLMS: ALIGN- ING LARGE LANGUAGE MODELS FOR TIME SERIES ANALYSIS
Zongjiang Shangh Dongliang Cui Binqing Wu Ling Chen 12x
State Key Laboratory of Blockchain and Data Security, Zhejiang University
2 College of Computer Science and Technology, Zhejiang University [zongj jijjaraaa angshang, runnercdl, binqingwu, lingchen]@cs: weu.uuu..ccuuu... edu.cr
ABSTRACT
Recently, there has been great success in leveraging pre-trained large language mod- els (LLMs) for time series analysis. The core idea lies effectively aligning the modality between natural language and time series. However, the multi-scale struc- tures natural language and time series have not been fully considered, resulting in insufficient utilization of LLMs capabilities. To this end, we propose MSH- LLM, a Multi-Scale Hypergraph method that aligns Large Language Models for time series analysis. Specifically, hyperedging mechanism designed enhance the multi-scale scale semantic information of time series semantic space. Then, a cross-modality alignment (CMA) module is introduced align the modality between natural language and time series at different scales. In addition, mixture of prompts (MoP) mechanism introduced to provide contextual information and enhance the ability of LLMs to understand the multi-scale temporal patterns time series. Experimental results on 27 real-world datasets across 5 different applications demonstrate that MSH-LLM achieves the state-of-the-art results.
INTRODUCTION
Time series analysis is a critical ingredient in myriad of real-world applications, e.g., forecasting Liu
сt al.
2023b Wan et al. 2024 Shang et al. (024a), imputation (Wang et al. (004a), ) and classification (Chen et al. 2024b Wang et al. (024c). which is applied across diverse domains, including retail, transportation, economics, meteorology, healthcare, etc. In these real-world applications, the task- specific models usually require domain knowledge and custom designs (Chen et al. [2024a] Zhou al. 2023a). This contrasts with the demand of time series foundation models, which are designed to perform well in diverse applications, including few-shot learning and zero-shot learning, where minimal and no training data provided.
Recently, pre-trained foundation models, especially large language models (LLMs), have achieved great success across many fields, e.g., natural language processing (NLP) Touvron et al. 2023 Achiam et al. 2023 Radford et al. (02T) and computer vision (CV) (Wang et al.
2024b Pi et al.
2024). Although the lack of large pre- pre-training datasets and a consensus unsupervised objective makes difficult to train foundation models for time series analysis from scratch (Sun et al. 2024 Jin et al. 2024. Pan et al. 2024), the fundamental commonalities between natural language and time series sequential structure and contextual dependency provide avenue to apply LLMs for time series analysis. The core idea lies in the effective alignment of the modality between natural language and time series, either by reprogramming the input time series (Xue & Salim 2023 Cao et al. 2024) by introducing prompts to provide contextual information for the input time series (Sun et al. 2024
Kamarthi & Prakash 2023 Jin al. 2024).
In the process of aligning L.MS for time series analysis, we observe that both natural language and time series present multi-scale structures. In natural language, multi-scale structures typically manifest as semantic structures at different scales (Yang et al. 2024b). e.g., words, phrases, and
"Coreesooningg author.

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sentences. In time series, the multi-scale structures often demonstrate multi-scale temporal patterns (Wen et al., 2021 Liu et al.] 2021] Shang et al.) 2024a). For example, influenced by periodic human activities, trafni volume and energy usage exhibit pronounced daily patterns (e.g., afternoon or evening) and weekly patterns (e.g., weekday or weekend). Considering multi-scale alignment between natural language and time series enables models to learn richer representations and enhance their cross-modality learning abilities. However, we argue that performing multi-scale alignment is a non-trivial task, as two notable problems need to be addressed.
The first problem lies in the disparity between the multi-scale semantic space of natural language and that of time series. The multi-scale semantic space of natural language is both distinctive and informative (Pan et al. (022)) while the multi-scale semantic space of time series faces the semantic information sparsity problem due to an individual time point containing less semantic information (Shang et al. 2024b; Chang et al. 2024). This disparity makes it difficult to leverage off-the-shel LLMs for time series analysis. To tackle this, most existing works employ patch-based methods (Nie al. 2022 Jin et al.) (2244) to capture group-wise interactions and enhance the semantic information of time series semantic space. However, simple partitioning of patches may introduce noise interference and make it hard to discover implicit interactions.
The second problem when performing multi-scal. alignment lies in the knowledge and reasoning capabilities to interpret temporal patterns are not naturally present within the pre-trained LLMs. To unlock the knowledge within LLMs and activate their reasoning capabilitiess for time series analysis, existing methods introduce prefix prompts (Jin et al.) [024] Liu et al.) 2024) self-prompt mechanisms (Sun et al. 2024) to provide task instruction and enrich the input contextual information. While these methods are intuitive and straightforward. they struggle to understand temporal patterns due to their failure to leverage multi-scale temporal features, Therefore, it is still an open challenge to design prompts that are accurate, dorr-cor,,,a and specific..
To this end, we propose MSH-LLM, a Multi-Scale Hypergraph method that aligns Large Language Models for time series analysis. To the best of our knowledge, MSH-LLM is the first multi-scale alignment work for time series analysis, which leverages the hyperedging mechanism to enhance the multi-scale semantic information of time series and employs the mixture of prompts mechanism to enhance the ability of LLMs in understanding multi-scale temporal patterns. Our contributions are given as follows:
We develop a hyperedging mechanism that leverages learnable hyperedges to extract hyper- edge features with group-wise information from multi-scale temporal features, which can enhance the multi-scale semantic information of time series semantic space while reducing irrelevant information interference.
We propose cross-modality alignment module perform multi-scale alignment based on the multi-scale prototypes and hyperedge features, which goes beyond relying solely on single-scale alignment and obtains richer representations. In addition, we design a mixture of prompts (MoP) mechanism, which augments the input contextual information with different prompts enhance the reasoning ability of LLMs for time series analysis.
We evaluate MSH-LLM on 27 real-world datasets across 5 different applications. The experimental results demonstrate that MSH-LLM consistently outperforms existing methods, highlighting its effectiveness in activating the capability of LLMs for time series analysis.
RELATED WORK
In-Modality Learning Methods. Recent studies in NLP (Devlin 2018 Radford et al., 2019 Brown
2020 Touvron et al.) 2023) and CV (Touvron et al. 2021 Wang et al. 2023 Bao et al. (022) ) have
shown that pre-trained foundation models can be fine-tuned for various downstream tasks within the same modality, significantly reducing the need for costly training from scratch while maintaining high performance. BERT (Devlin) 2018 uses bidirectional encoder representations from transformers to recover the randomly masked tokens of the sentences. GPT3 (Brown) 2020) trains a transformer decoder on a large language corpus with much more parameters, which can be utilized for diverse applications. BEiT (Bao et al. 2022 designs masked image modeling task to pretrain vision transformers. Motivated by the above, recent time series pre- pre-trained models use different strategies, e.g., supervised learning methods (Fawaz et al. 2018) or self-supervised learning methods (Chen
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et al. 2025 Woo et al., 2022a), to learn representations across diverse domains and then fine-tune on similar applications to perform specific tasks. However, due to the lack of large pre- pre-training datasets and consensus unsupervised objective, it is difficult to train foundation models for general-purposs time series analysis that covers diverse applications.
Cross-Modality Learning Methods. Due to the fundamental commonalities between natural language and time series in sequential structure and contextual dependency, recent works have explored cross-modality learning by applying LLMs for time series analysis (Bian et al. 2024 Zhou
et al. 2023a Liu et al. 2024 Jin el al. (2024). FPT (Zhou et al., (023a) represents an early attempt fine-tune key parameters LLMs and adapt them for time senes analysis. aLLM4TS (Bian et al.
2024) introduces a two-stage pre- pre-training strategy that first performs causal next-patch training and then enacts a fine-tuning strategy for downstream tasks. However, fine-tuning L.MMs for training and inference can sometimes be desource-consumigg due to the immense size of L.LMs (Liu et al. 2024). Some recent works have explored the alignment of frozen LLMs for time series analysis, either by reprogramming the input time series or introducing prompts to provide contextual information for the input time series. Time-LLM (Jin et al., 2024) proposes a reprogramming strategy that aligns time series imputs with textual prototypes before passing them to frozen LLM. Auto'Times (Liu et al. 2024) repurposes frozen LLMs as autoregressive time series forecasters and introduces relevant time series prompts enhance forecasting. Although these methods achieve promising results, they overlook the multi-scale structures of natural language and time series.
Multi-Scale Time Series Analysis Methods. Existing multi-scale time series analysis methods are aimed at modeling temporal pattern interactions at different scales (Chen et al. 2021] Shang al. 2024b Chen al. 2023b). TAMS-RNNs (Chen et al. 2021) disentangles input series into mulu-scale representations and uses different update frequencies to model multi-scale temporal pattern interactions. Benefiting from the attention mechanism, transformers achieve promising results in time series analysis. Pyraformer (Liu et al. (2021) treats multi-scas scale features as nodes and leverages pyramidal attention to model interactions between nodes at different scales. To solve the problem of semantic information sparsity, Pathformer (Chen et al.) 2023b) divides time series into multiple resolutions using patches of different sizes and uses the dual attention to capture group-wise pattern interactions at different scales. MSHyper (Shang et al. (024b) combines Transformer with multi-scale hypergraphs to capture group-wise pattern interactions across different temporal scales. However, fixed segments pre-defined rules cannot capture implicit pattern interactions and may introduce noise interference.
In this paper, find that both natural language and time series present multi-scale structures. Therefore, we propose a multi-scale hypergraph method that aligns large language models (LLMM) for time series analysis. Specifically, hyperedging mechanism is introduced to enhance the multi- scale semantic information of time series semantic space and reduce noise interference. Then, a cross-modality aligument (CMA) module is introduced to perform multi-scale alignment. addition, -oroooopss-s multi-scale temporal patterns.
we develop mixture-of prompts (MoP) strategy to strengthen the reasoning ability of LLMs over
3 PRELIMINARIES
Hypergraph. A hypergraph can be represented as G (V,E), where V= {w represents the set of nodes and È = {e, 1 denotes the set of hyperedges. Unlike simple graphs, each hyperedge characterizes group-wise interactions by encompassing an arbitrary subset of nodes. The structural topology can represented by the incidence matrix H E NxM where H = 1 signifies the membership of node Un in hyperedge Cyn and Ham = otherwise. More descriptions of hypergraph learning are provided in Appendix (
Problem Definition. The proposed MSH-LLM designed to align frozen LLMs for time series analysis, which covers different applications across various domains. For given specific application that consists the input time series X E Tx with T time steps and D dimensions, the goal of time series analysis is to predict important properties of the time series. For example, the forecasting task aims at predicting the future steps X E IR IDD, while the classification task aims at predicting the class labels of the given time series.
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Ail Lew hor
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Alges Fleraes
Hopere Hollanhe
Extodorr
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Exnritioe Madda
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Figure I: The framework of MSH-LLM. (a) and (b) provide detailed delincation of the multi-scale extraction module, while (c) elaborates on the hyperedging mechanism.
METHODOLOGY
MSH-LLM aims to repurpose frozen large language models (LLMs), such as LLaMA Touvron et al. 2023) and GPT-2 (Radford al. 2019). for caneral-purposs time series analysis. This is achieved by considering the multi-scale structures inherent both natural language and time series data. As depicted in Figure 1 the time series data and word token embeddings (derived from pre-trained LLMs) are mapped into multi-scale temporal features and text prototypes, respectively. Then, hyperedging mechanism developed enhance the multi-scale semantic information of time series semantic space, and cross-mas moddlltyyy alignment (CMA) module is introduced to align the modality between natural language and time series. addition, a mixture of prompts (MoP) mechanism is introduced to provide multi-scale contextual information and enhance the ability of LLMs in understanding multi-scale temporal patterns of time series.
4.1 MULTI-SCALL EXTRACTION (ME) MODULE
The ME module is designed extract the multi-scale features, which include hicrarchical temporal features extraction and hierarchical text prototypes extraction.
Hierarchical Temporal Features Extraction. As illustrated in Figure I given input time series X = X we first normalize it through reversible instance normalization (Kim et al. (021). Then, we perform hierarchical temporal features extraction, which can be formulated as tollows:
X = (00(("=1,0000 EERRN ND 2 2,
(I)
where X = {0jjææ E TR, PRE [T] denotes the feature set at scale 8, = 2..., S denotes the scale index, and S is the total number of scales. Agg is the aggregation function, such as 1D convolution or average pooling. 8--1 denotes the learnable parameters the aggregation function at scale x 1, N 1 aggregation window at scale 1.
--r the sequence length at scale 8, and 1--1 denotes the size of the
Hierarchical Text Prototypes Extraction. The hierarchical text prototypes extraction aims to map word token embeddings in natural language multi-scale structures, e.g., words, phrases, and sentences, for alignment with multi-scale temporal features. As shown in Figure [bb), given the word token embeddings based pre-traie trainedne LLMs U IR VxP, where V is the vocabulary size and P the hidden dimension of LLMs. We first transform them to a small collection of text prototypes through linear mapping, which can be represented as U1 & V where V' & V. This approach is efficient and can capture key linguistic signals related time series. Then, we can derive multi-scale text prototypes via linear mapping, as formulated below:
U Linear Linear(U** (;=1) E
ERV' XP
8 2,
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where Linear denotes the linear mapping function, U denotes the text prototypes at scale while X== refers to the learnable parameters of the linear mapping function at scale 1. After mapping, aim for the multi-scale text prototypes to capture the linguistic signals that describe multi-scale temporal patterns. Experimental results in Appendix [ validate the effectiveness of the multi-scale text prototype extraction compared manually selected approaches.
4.2 HYPEREDGING MECHANISM
After obtaining the multi-scale temporal features and text prototypes, straightforward way to align LLMs for time series analysis perform cross-modality alignment at different scales. However, the semantic space disparity poses significant challenge, making it difficult to leverage the off-the- LLMs for time series analysis. To tackle this, some recent studies (Jin al. 2024 Shang al.] 2024a) show that group-wise interactions can help enrich the semantic information of time series semantic space, thereby enhancing its consistency with the semantic space of natural language. Therefore, introduce hyperedging mechanism that utilizes learnable hyperedges to capture group-wise interactions at different scales.
As depicted in Figure [c), first treat multi-scale temporal features nodes and initialize two kinds of learnable embeddings at scale 8, i.e., hyperedge embeddings Buyper E T x and node embeddings E Eaode N=,* where M is a hyperparameter that defines the number of hyperedges at scale 8. Then, the similarity calculation is performed to construct the scale-specific incidence matrix H which can be formulated follows:
U tanh E 3):
U Lanh( Buyper (p),
H Linear(ReTLU
where B R1x1 and & E 1 are learnable parameters. The tanh activation function is used to perform nonlinear transformations and the LU activation function is applied to eliminate weak connections. To enhance the robustness of the model, reduce the computation cost of subsequent operations, and mitigate the impact of noise, we introduce a sparsity strategy to make H sparse, which can be formulated follows:
{
1, H E TopK H (....
H
0,
H $ Topk (Topk(H,,:.
where 77 is the threshold of T T'opK function and represents the maximum number of neighboring hyperedges associated with node.
The final scale-specific incidence matrices can be represented (H'... .......T H and the hyperedge features of the ith hyperedge e, E Ex based on the scale-specific incidence matrix at scale s is formulated as follows:
Aug( Σ x3EN(c)))) -
(5)
where Ang is the average operation, .N(e²) is the neighboring nodes connected by ef at scale 8, and 21 E X represents the jth node features scale The final hyperedge feature set different scales
can be represented as {E-, (E......,e ..... .. E...
Compared with other methods, our hyperedging mechanism is novel in two aspects. Firstly, our methods can capture implicit group-wise interactions at different scales in a learnable manner, while most existing methods (Nie al. 2022; Zhou al., 2023a| Shang al. (024b) rely on pre-defined rules to model group-wise wise interactions at single scale. Secondly. although some methods (Shang al., [0244a] Diang et al. 2019) learn from hypergraphs, they focus on constraints or clustering clustering-based based approaches to learn the hypergraph structures. In contrast, MSH-LLM learns hypergraph structures data-driven way by incorporating learnable parameters and nonlinear transformations, which more flexible and can learn more complex hypergraph structures.
4.3 CROSS-MODALITY ALIGNMENT (CMA) MODULE
The CMA module is designed to align the modality between natural language and time series based on the multi-scale hyperedge features and text prototypes. To achieve this, multi-head cross-attention
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is used to perform alignment at different scales. Specifically, for the given text prototpyes U and hyperedge features Ex at scale s, we first transform it into query Q; = E"W,, 4,7" key K = U and value V U Vi respectively, where J 1, ..... J denotes the head index. W E D WE, Pxd and W E PRd are learnable weight matrices at scale 8, d 1 Then, the multi-head cross-attention is applied to align the hyperedging features with text prototypes, which can be formulated as follows:
k.
(K5) Va
3 = Allm( K;, V2) = softmart
V
(6)
Then, we aggregate 3 in every head to obtain the output of multi-head attention 2" E RMM MM x at scale s. The final aligned features at different scales can be represented as {z', ,.., ,..... 2
4.4 MIXTURE OF PROMPTS (MoP) MECHANISM
The performance of LLMs depends significantly on the design of the prompts used to steer the model capabilities (Pan et al. 2024 Zhou et al. 2023b). To enhance the reasoning capabilities of LLMs, most existing methods focus on prefix prompts (Jin et al. 2024 Liu al. 2024) or self- prommpt mechanisms (Sun et al. 2024 Lester et al. 2021) to provide task instructions and enrich the input contextual information. However, the prompts affecting the reasoning capabilities of L.IMS are multifaceted. Relying on single type of prompt cannot fully activate the reasoning capabilities of LLMs. Therefore, we propose a MoP mechanism, which augments the input contextual information with different prompts (i.e., Iearnable prompts, cata-correlated prompts, and capability- prompts) and enhances the reasoning capabilities of LLMs towards multi-scale temporal patterns.
Learnable Prompts. Learnable or soft prompts show great effectiveness across many fields by utilizing learnable embeddings, which are learned from the supervised loss between the output of the model and the ground truth. However, existing learnable prompts cannot capture the temporal dynamics from multi-scale temporal patterns.
Therefore, we introduce multi-scale learnable prompts C, = (P',.... P P',.... PS), where p E RR4*DD XD is the scale-specific prompts and L is the prompt length at scale C, learns from the loss between the output of LLMs and tpsc-spec ground truth.
Data-Corred Correlaeee Prompts. As shown in Figure 2aa), we introduce three components to construct data-correlated prompts Ca. i.e., data description (π), task introduction (r), and data statistics (μ). The data description provides LLMs with essential background information about the input time series, the task introduction is used to guide LLMs in understanding and performing specific tasks, and the data statistics provide time series statistics that include both input sequence and sub-sequences at different scales. The final cora-correl prompts can be formulated as follows:
C. LLMs S.LMs(tooenize)) (tokenizer(,,
-enbbliit--Eaaanning Prompts. Some recent studies in NLP (Kojima et al. 2202) and CV (Ge al. 2023) have shown that prompt engineer- ing, e.g., template and chain-
(Data Description) The Elecricity Transformer Terpaaatur (E catasess are from XXXX
(). Think suep step
prompts, can significantly enhance the reasoning abilities of LLMs, especially for few-shot or zero-shot learning. We have observed similar rules when aligning LLMs for time series analysis. There- fore, as shown in Figure bb. we design three components construct capability-enhanede prompts C.,, i.e., logical thinking (Ø), emotional manipulation (1)), and time series reasoning correlated prompts (.)). The logical thinking prompts guide LLMs to solve problems in a step-by-step manner, which may enhance the multi-step reasoning abilities of LLMs; The emotional manipulation prompts mimic the impact of emotions on human decision-makee
[Thok Inemuttinn:: Groe the prrviots ps task prodic the sest steps....
( "Thee's really importan for me
[Du statonies) The namber of sesle is sun. be max voluec is con the min voltac is
Cosiddiin ARIMA (AunoRagresivve Integgnied Mesing Avergg))
Prompt
Copphiiig--ohhaniinggg Procept
Figure 2: Prompt example. and < are task- specific configurations and input statistic informa- tion, respectively.
using "emotional blackmail" to make the model focus more on the current task; The time series reasoning correlated prompts provide specific methodologies that help LLMs to deal with temporal features. The final capability-enhancing prompts are formulated as follows:
C. LLM s ((okenizer sp, )))
(8)

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4.5 OUTPUT PROJECTION
After obtaining the MoP, we first concatenate the learnable prompts with the aligned features at different scales, then concatenate them with data-correlated prompts and cupability-enhaneee prompts and put them into LLMs to get the output representations, which can be formulated as follows:
O LLMM Ce. [P', I......
where [ denotes the concatenation operation. Then, we obtain the final results through linear mapping and instance denormalization.
5 EXPERIMENTS
Experimental Settings. We conduct experiments on 27 real-world datasets across 5 different appli- cations to verify the effectiveness of MSH-LLM, including long/short-term time series forecasting, classification, few-shot learning, and zero-shot learning. Overall, MSH-LLM achieves state-of-the-ar results in a range of critical time series analysis tasks against 19 advanced basclines. More details about baselines, datasets, and experiment settings are given in Appendix A C and D respectively.
5.1 LONG- TERM FORECASTING
Setups. For long-term time series forecasting, evaluate the performance of MSH-LLM on 7 commonly used datasets, including ETT (i.e., ETThI, ETTh2, ETTml, and ETTm2), Weather, Traffic, and Electricity datasets. More details about the datasets are given in Appendix C Following existing works (Jin et al. 2024 Zhou et al.] [2023a] Pan et al. (2024). we set the input length 7 512 and the forecasting lengths // (96, 192, 336, 720). The mean square error (MSE) and mean absolute error (MAE) are set as the evaluation metrics.
Table I: Long-term time series forecasting results. Results are averaged from all forecasting lengths. Smaller values correspond to better performance. Bolded: Best, Underlined: Second best. Full
results are listed in Appendix F.I. Table 8
L1MM
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DOME 2004
(K3.B 2001
(NeantS 2003)
(KLR 2004)
DMAA 2107)
(XC.R 21C3)
MSR MAP
VISE MAR
MISI MAP
MAR
MAR
MSR.MAF
NSR MAR
MSR MAR:
MAR
MSB
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0:11
0.310.159
0230
0.366
0.335
0.71
3.30
0.25902277
6.55/9225
0089 8226
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8J67 0.63
6237
0.102 8.255
0.20 1.204 1.394
0.178 0.778
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Results. Table [ summarizes the results of long-term time series forecasting. We can observe that: (1) MSH-LLM achieves the SOTA results in all datasets. Specifically, MSH-LLM achieves an average error reduction of 10% and 3.72% compared LLM4TS methods (i.e., LLLM,,,, AutoTimes, Time-LLM, and FPT), 8.54% and 6.45% compared to the latest Transformer- methods (i.e., ASHyper, i'Transformer, and MSHyper), and 7.48% and 5.58% compared to the Linear-based methods (i.e., AMD and DLinear) in MSE and MAE, respectively. (2) Competitive performance is achieved by Ada-MSHyper, MSHyper, and PatchTST through the consideration of group-wiss wise interactions. (3) LLM4TS methods (e.g., 1-IM-LLM and Time-LLM) further improve upon these by embedding group-wise interactions into LLMs. However, they overlook the multi-scale structures of natural language and time series. (4) By considering the multi-scale structures of natural language and time series, MSH-LLM outperforms other LLM4TS methods in almost all cases.
5.2 SHORT- TERM FORECASTING
Setups. To fully evaluate the performance of MSH-LLM, we also conduct short-term forecasting experiments on M4 datasets, which contain marketing data with different sampling frequencies. More details about M4 datasets given in Appendix C The forecasting lengths set between 6 and 48, which are significantly shorter than those in long-tem term time series forecasting. Following existing works (Zhou et al.) 2023a Jin et al. 2024 Pan et al. 2024), we the input length be twice the
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forecasting length. The symmetric mean absolute percentage enror (SMAPE), mean absolute scaled error (MASE), and overall weighted average (OWA) are used as the evaluation metrics.
Table 2: The mean performance results for short-term time series forecasting on the M4 datasets. Smaller values correspond to better performance. Bolded: Best, Underlined: Second best. Full results are listed in Appendix F.2 Table 9
MSBHIM
Auartienes
S'IP-LM 2024)
Tmm-1M
FIPT
Dilnear
PlesTTT
NAHTTS
N-HEATS
Tirannie
SIAAPs
11,89 11.31
12
12,444 1.31
12,50 500
12,162
13,779
12,009 1,03
12,05
12.35
12.85
MASE
1.877
1.55
1412
1,31
1635
1.666
Results. Table 2 gives the short-term time series forecasting results. We can see that: (1) MSH- LLM performs slightly better than Auto'Times and substantially exceeds other baseline methods. (2) By leveraging L.LMs and Patch mechanisms, AutoTTime: and PStchTST achieve competitive results than other baseline methods. (3) Compared to AutoTimes and PatchTST, MSH-LLM achieves superior performance, the reason may be that the hyperedging mechanism can enhance the multi-scale semantic information of time series semantic space while reducing irrelevant information interference.
5.3 TIME SERIES CLASSIFICATION
Setups. We also perform the time series classi- fication task to verify the generalization abil- ity of the model. Following existing works (Zhou et al., 2023a Wu et al., 2022), we use 10 multivariate UEA time series classification datasets for evaluation, which cover different do- mains (e.g., gesture, medical diagnosis, and au- dio recogmition). More details about the datasets are given in Appendix Accuracy is used as the evaluation metric.
MSBLLX
SimaNo
1.0
Finelasse
13.0
Roded
7335
liforee
LSTNe
1.30
ErSiaamee
Pysfomer
10
FEDiume
ICN
Results. Figure 3 shows time series classifica- tion results. MSH-LLM achieves an average accuracy of 75.38% surpassing all baselines in- cluding advanced LLM4TS methods FPT (74%). is also notable that other methods considering multi-scale structures (e.g., TimesNet and Flow- former) can also achieve better performance. The reason is that the time series classification is sequence-level task, and multi-scale structures help models learn hierarchical representations. However, MSH-LLM still performs better than those methods, the reason may be that MSH-LLM leverages MoP mechanism to enhance the reasoning capabilities of LLMs, thereby promoting LLMs to learn more comprehensive representations of multi-scale temporal patterns.
(79)
Figure 3: Time series classification results. The re- sults are averaged from 10 subsets of UEA. Higher values mean better performance. Full results are given in Appendix F.3
5.4 FEW-SHOT LEARNING
Setups. LLMs have shown impressive capabilities for few-shot learning (Liu et al. 2023a). Following existing works (Jin et al. 2024 Zhho et al.) 2023a), we use limited training data (i.e., 5% and 10% of the training data) on 7 commonly used datasets to evaluate the few-shot learning performance.
Table 3: Few-shot learning results with 5% of the training data, averaged across all forecasting horizons. Bolded: Best, Underlined: Second best. Full results are given in Appendix F4 Table
Mashaas
MSH
(Ousi
S'BLLLM (CML 3024)
OCLR 2054)
Time LLM
(NeutPS 2023;
EPT
(CCR
PaNAISTT
2003)
TaneS (ICLR 3023)
FEDiener 2002)
(Neu 3013)
JSS
(w 3025)
ETSfomer
(NeurIPS
Autobora
Meric
MSs: MAR.
Mip MAR
MSR MAR
MSR MAR
MSR MAR:
Msn MAIl
MSB MAR
MSEU
MSR MAII
MSH MAR:
MSS MAR:
Woather
0247 0.281
0.6400300
0.330030
0.309
0.269 0.305
0.208 318
0.309
0.3106.355
0.337 0.327..315 0.35
0.33
0.34604404
0663
Traltic
8.133 0.392
04206.299
0.423 0.302
0,44
64500.324
D4418 0.20m:
(L867 0.493
IN/0 14423
0.833 0L502
1,525 0.39
0392)
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Results. Table summarizes the few-shot learning results under 5% training data. We can that MSH-LLM achieves SOTA results in almost all cases, reducing the average prediction error by 10.47% and 6.74% over other LLMM4T methods (i.e., I'I.-L.MM and Time-LLMM in terms of MSE and MAE, respectively. This may be attributed to the fact that MSH-LLM can consider the multi-scale structures of natural language and time series, while using the MoP mechanism to unlock the knowledge within LLMs to understand multi-scale patterns. The few-shot learning results under 10% training data are given in Appendix 5.4
5.5 ZERO-SHOP LEARNING
Sctups. In this section, we evaluate the performance of MSH-LLM for few-shot learning, where no training sample of the target domain is available. Specifically, we adhere to the benchmark established by (Zhou al., 2023a] Liu et al., 2024) and evaluate the catssed adaptation performance (i.e., the model's 's performs on dataset A when trained on dataset B). M3 and M4 datasets are used to evaluate the zero-shot learning performance.
Table 4: Zero-shot learning results in terms averaged SMAPE. Bolded: Best, Underlined: Second best. Full results are listed in Appendix F.3
Table [4
Methods
MSH-LLM (0m))
INeurIPS 2004)
(NeariP
(MAM 2021)
PAsITST
TimcsNet OULR
(Nevri'S 2003)
NStormer
(MAA) 2011
(ULR 2019)
M4 XMS
12
13,000
14,000
15,500
14.170
13:530
13,300
MD -M4
12968
13,36
13.325
15.357
132231
14355
14.327
15.047
19,047
14,092
Results. Table 4 provides the zero-shot learning results. MSH-LLM consistently outperforms other methods zero-shot scenarios. Both M3 and M4 pose significant challenges due to their sophisticated multi-scale characteristics and diverse domain distributions. MSH-LLM still achieves the best performance, giving an average of 10.23% SMAPE error reductions across all baselines on average. This improvement may stem from its ability to leverage the reasoning capabilities of LLMs for interpreting multi-scale temporal patterns.
5.6 ABLATION STUDIES
Scaling law is essential characteristic that extends from small models to large foundation models. To investigate the impact of backbone model size, we design the following three variants: (1) Using the first 12 Transformer layers of LLaMA-7B (L.12). (2) Replacing LLaMA-7B with GPT-2 Small (G.12). (3) Replacing LLaMA-7B with the first 6 Transformer layers of GPT-2 Small (G.6). The experimental results the Traffic dataset are shown in Table We can observe
LLMs Selection.
that MSH-LLSH LLM (Default 32) performs better than L.12, G.12, and G.6, which indicate that the scaling law also applies to cross- coos--oodalssiss alignment with frozen LLMs.
Table 5: Results of different LLMs selection and MoP mechanism.
The best results are bolded.
Methods
6.6
-wo C:
Ca1
<wto
MSH-LIM (Default 32)
Meuic
MSE MAE
MSE MAE
MSE MAE
MSE MAE
MSE MAB
MSE MAE
MSE MAB
MSE MAE
96
0.274
0.377 0.281
0.393 0 205
0.373 0.272
0.368 0.273
0.375
0.399..283
(L365 0.270
192
0.375 0.283
0.385 0.290
0.404 0 207
0.379 0.289
0.383 0.286
0.392
0.403 0.290
(L372 0.281
0.393
0.397
0.400
0.405 0.292
0.391 0.284
0.409
0L385 0.279
MoP Mechanism. To investigate the impact of MoP mechanism, we design three variants: (1)
Removing the learnable prompts (-w/o Ci). (2) Removing the data-correlated prompts (-w/o Ca). (3) Removing the aopability-enhancin prompts (-w/o C.). (3) Removing the MoP mechanism (-w/o
MoP). The experimental results on the Traffic dataset are shown Table [ from which we can observe that MSH-LLM performs better than -w/o C,, -w/o C. and -w/o C., showing the effectiveness of learnable prompts, Lata-correlater prompts, and capability-enhancing prompts, respectively. In addition, -wo MoP achieves the worst performance, demonstrating the effectiveness of the MoP mechanism. More ablation experiments on the MoP mechanism, hyperedging mechanism, ME module, and CMA module shown in Appendix Gand

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Published as a conference paper at ICLR 2026
5.7 VISUALIZATION
Visualization of the MoP Mechanism. To investigate how prompts can guide LLMs in time series analysis. The t-SNE results on the Traffic dataset are provided in Figure 4 We can observe that output of LLM with the MoP mechanism (Figure a)) shows distinct clusters, while the output of LLM without the MoP mechanism (Figure 4e)) reveals a more spread-out and lacks clear clustering. The experimental results show the effectiveness of the MoP mechanism in activating the abilities of LLMs to capture multi multi-scale temporal patterns. In addition, we observe that Figures 4a) and Figure bb (-w/o C₁) share similar clusters, while Figures 4cc) (-w/o C₁) and Figure (-w/o C.) show less distinct clusters compared to Figure a)), a), suggesting that Ca has a relatively minor influence compared C₁ and C. the Traffic dataset for long-term forecasting. However, this does not imply that C is unimportant, as removing Ca leads to a performance degradation. The qualitative analysis
also aligns with the experimental results in T
(a) MSH-LLAN LLM
(b) -w/o Ca
(c) -w/o C.
(d) -w/o C
(e) -w/o MoP
Figure 4: t-SNE visualization of pre-trained LLM outputs under different prompts.
Visualization of the Weight Between Text Prototypes and Word Embeddings. To investigate whether different text prototypes possess explicit semantic meanings, we conduct qualitative analysis by visualizing the similarity scores between 10 randomly selected text prototypes and word embed- dings derived from 3 different word sets. The visualization results on the ETTh1 dataset are given in Figure 5] We can discern the following tendencies: 1) Prototypes 2, 3, 7, and exhibit strong associations with word set (noun-like time series descriptions), while prototypes 0, 1, and 4 show strong correlations with word set 2 (adjective-like time series descriptions). This suggests that the prototypes capture different semantic roles, indicating explicit semantic differentiation. 2) Although both word set 1 and word set 3 consist of noun-like descriptions, almost all prototypes show weak correlations with word set (name-related words). The reason may that the text prototypes encode time-series- speessii, context-specific semantic information. The experimental results show that the text prototypes possess explicit meaning.
Ward (ppaak "noaggh, www.ggtarttty......
Ward Poyytica,, 'anwnth Word "tatlr, Meskt
Wird Se:
Word 2
Word Set
Figure 5: The visualization of the weight between text prototypes and word embeddings.
CONCLUSIONS
In this paper, present MSH-LLM, multi-scale hypergraph framework that aligns pre-trained large language models for time series analysis. Empowered by the hyperedging mechanism and cross- modality alignment (CMA) module, MSH-LLM can perform alignment different scales, addressing the problem of multi-scale semantic space disparity between natural language and time series. Then, mixture of prompts (MoP) mechanism is introduced to enhance the reasoning capabilities of LLMs towards multi-scale temporal patterns. Experimental results on 27 real-wood datasets across different applications demonstrate that MSH-LLMM consistently outperforms existing methods.
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7 ACKNOWLEDGEMENT
This work was funded by the National Key Research and Development Program of China (No.2024YFB3312900),
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A DESCRIPTION OF BASELINES
To evaluate the effectiveness of MSH-LLM, we select 17 advanced baselines for comparison. The detailed descriptions of these baselines are given as follows:
Auto AutoTimes (Liu al. 2024): AutoTTimes repurposes L.MS as autoregressive time series forecasters
by projecting time series into language token embeddings and utilizing in-context forecasting.
Time-LLM (Jin al., 2024): Time-LLM is a reprogramming framework that repurposes large language models (LLMs) for time series forecasting by aligning time series data with text prototypes and enhancing the model's 's reasoning ability through Prompt-as Frompt-as-Prehh Prefix (PaP) mechanism.
FPT (Zhou et al. 2023a): FPT leverages pre- pre-trained language and CV models for time series analysis by fine-tuning them without altering their self-attention and feedforward layers.
S'IP-LLM Pann et al. 2024): I'IP-LLM aligns the pre-trained semantic space of LLMs with time series embeddings by designing a cross-modality tokenization module and maximizing cosine similarity between semantic anchors and time series components, enabling improved time series forecasting through learned prompts.
DLinear ZZeg et al.
2022): DLinear uses the simple one-layer model to capture temporal dependen-
cies of different decomposed components.
N-HITS [Caallu al.
N-HiTS proposes novel hierarchical interpolation and multi-rate data sampling techniques to model multi-scale temporal patterns.
2023
N-BEATS (Oreshkin al. 2020): N-BEATS employs a deep stack of fully-connected layers
capture temporal dependencies.
AMD (Hu al. 2025); AMD decomposes time series into distinct temporal patterns at different scales and leverages the multi-scale decomposable mixing block to dissect and aggregate these patterns residual manner.
(Shang et al. 2024a): : Ada-MSHyper utilizes an adaptive hypergraph to capture multi-scale group-wise interactions, while introducing the node and hyperedge constraint mechanism to address the issue of entangled temporal variations.
Ada-MSHyper
iTransformer (Liu et al. 2023b:: iTransformer inverts the Transformer's input dimensions, applying attention and feed-forward networks on variate tokens to capture cross- variate correlations and improve long-sequence forecasting.
PatchTST (Nie et al., 2022): PatchTST proposes a Transformer-based model for multivariate time series forecasting by segmenting time series into subseries-level patches and applying channel- independence strategies.
TimesNet (Wu et al., 2022): TimesNet models complex temporal variations in time series by transforming ID series into 2D tensors.
MSHyper (Shang et al. 2024b): MSHyper combines the multi-scale feature extraction module with the pre-defined multi- scale hypergraph for group-wise pattern interactions.
Autoformer (Wu et al.
Autoformer introduces a decomposition architecture with an Auto- Correlation mechanism capture the long-range dependencies.
202I):
NSFormer (Liu et al.
2022): : NSFormer employs a set of stationarization modules and the de-
stationary attention to tackle the problem of vver-stationarization.
FEDformer [Zhou et al. 2022): :: FEDformer improves long-term time series forecasting by combining
seasonal-trend decomposition with frequency-enhance Transformer, capturing both global patterns and detailed structures efficiently with linear complexity.
ETSformer (Woo al. 2022b): ETSformer incorporates the principles of exponential smoothing by replacing self-attention with exponential smoothing and frequency attention for time series forecasting.
Reformer (Kitaev et al. 2019): Reformer improves Transformer efficiency by replacing dot product attention with lecality-sensitive hashing and using reversible residual layers.
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Informer (Zhou al., 2021:: Informer introduces ProbSparse self-attention mechanism, self- attention distilling, and generative-style decoder for uan-sequencee time series forecasting, address- ing time complexity, memory issues, and improving prediction efficiency and accuracy.
B DESCRIPTION OF HYPERGRAPH LEARNING
Compared to Graph Neural Networks (GNNs) (Chen et al. 2023a Chen & Zhao 2025 Chen & Chen 2024), which model pairwise interactions by operating graphs where each edge connects exactly two nodes, Hypergraph Neural Networks (HGNNs) (Shang et al. 2024a b Wu et al. 2025a) generalize this paradigm to capture group-war wise interactions through hyperedges that connect an arbitrary number of nodes. As shown in Figure O the graph is represented using the adjacency matrix, in which each edge connects nodes. In contrast, the hypergraph is represented by the incidence matrix, which can capture group-wise interaction using its degree-free hyperedges.
Groph::
Hypergaph
32
R6
Alscency BUIN
Gnpp dinatur
Incideeuc TERINS
Hepayyaph
Figure 6: The comparison between graph and hypergraph.
Recently, HGNNs have been applied in different fields, e.g., video object segmentation (Huang et al.
2009), stock selection (Sawhney et al.) (021), temporal knowledge graphs (Tang et al.) 2024). and time series forecasting (Shang et al. 2024a b Zhao et al.) [023 Tian et al. 2025). HyperGCC (Yadati et al.] 2019) is the first work to incorporate convolutional operations into hypergraphs, demonstrating the superiority of HGNNs over ordinary GNNs in capturing group-wise interactions. STHAN-SR (Sawhney et al. 2021) reformulates stock prediction as a learning-to utilizes hypergraphs capture group-wiss interactions between stocks. GroupNet (Xu et al. 2022) employs multi-scale hypergraphs for trajectory prediction, which combines relational reasoning with hypergraph structures capture group-wise pattern interactions among multiple agents. In the context of time series forecasting, MSHyper (Shang et al. (024b) is the first work to incorporate hypergraphs into long-term time series forecasting, which leverages predefined hypergraphs and the tri-stage message passing mechanism to capture multi-scale pattern interactions. Building on this, Ada-MSHyper (Shang al.) (222a) introduces adaptive hypergraph modeling, which combines adaptive hypergraphs with the node and hyperedge constraint mechanism to capture abundant and implicit group-wise temporal pattern interactions.
task and
In this paper, we represent temporal features of different scales as nodes and use learnable hyperedges in the hypergraph to capture group-win sooup interactions, thereby enhancing the semantic information of time series semantic space. We formulate this process as the hyperedging mechanism. As mentioned above, our hyperedging mechanism differs from previous methods two aspects. Firstly, our methods can capture implicit group- wise interactions at different scales in learnable manner, while most existing methods (Nic et al. 2022; Zhou et al.) 2023a Shang et al.) (024b) rely on pre-defined rules model group-wise interactions
single scale. Secondly, although some methods (Shang et al. 2024a Jiang et al. 2019) learn from hypergraphs, they focus on constraints or clustering-basee approaches learn the hy hypergraph structures. In contrast, our method learns the hypergraph structures in pure data-driven manner by incorporating learnable parameters and nonlinear transformations, which is more flexible and learn more complex hypergraph structures.
C DESCRIPTION OF DATASETS
To evaluate the performance MSH-LLM, we use 7 real-world datasets for long tong-term time series forecasting and few-shot learning. Detailed description of these detasets are given as follows:
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Electricity Transformer Temperature (ETT) datasets: These datasets are collected from power grid snapshots in two distinct regions of China. They encompass four subsets: ETThl and ETTh2 with an hourly sampling rate, and ETTml and ETTm2 with 15-minute sampling rate.
Traffic datase [ The Traffic dataset records the highway occupancy rates via 862 individual sensors. collected at a 1 --hour granularity. forming complex multivariate time series for evaluating long-term dependencies.
Electricity datase The Electricity dataset contains the hourly electricity consumption of 321 individual clients. It is recorded at a I-hour granularity.
Weather dataset The Weather dataset records 21 meteorological indicators collected from weather stations Germany, with observations recorded a 10-minute granularity throughout the year 2020.
In addition, we employ M4 datasets (including M4 Yearly, M4 Quarterly, M4 Monthly, M4 Weekly, M4 Daily, and M4 Hourly) for short-term forecasting and both M3 (including M3 Yearly, M3 Quarterly, M3 Monthly, and M3 Others) and M4 datasets for zero-shot learning. Detailed descriptions of M3 and M4 datasets are given as follows:
M3 datasets: The M3 datasets comprise 3,003 time series with varying granularities, including micro, industry, macro, and finance. It presents diverse collection of seasonal and non-seasonal sequences, with frequencies spanning from yearly monthly.
M4 datasets: The M4 datasets are larger than M3 datasets and expands the collection to 100,000 individual time series. The M4 datasets encompass vast array of temporal patterns across six distinct frequencies (ranging from hourly to yearly).
Finally, we use 10 multivariate datasets selected from the UEA time series classification Archive (Bagnall al. 2018 Zerveas et al.) 2021) for time series classification. These datasets are complex, which cover different domains (e.g.. gesture, medical diagnosis, and audio recognition) and exhibiting diverse characteristics in terms of sample size, dimensionality, and number of classes. The detailed descriptions of the datasets are provided in Table 6
Table 6: Dataset descriptions for time series classification. The dataset size is organized in (training, validation, and testing).
Dataset size
Vasiates
Classes
Information
CahanoiConcettratan
(261, 0, 263)
Biomedical
FaceDetection
(5890, 0, 3524)
144
Computer Vision
Handwriing
(150, 850)
26
Puttenn Recognition
(204, 205)
61
Medical Recognition
Japancse Vowels
(270, 0, 370)
12
Audio Reagnition
PEMS-SF
(267, 0, 173)
963
Transporation
SelfRegooaiioSCCP)
(268, 293)
Psychology
SelfRegubttinSCC2
(200, 0, 180)
Psychology
SpolenArabiccigiis
(6599, 0, 2199)
13
10
Speech Revognitio
UWgreGeeturLLibrryy
(120, 0, 320)
Gesture
We follow the same data processing and sliniing-alidaation-tssstt split protocol in existing works
(Zhou et af. 2023a; Jin et al.] 22044 |aan et al. 20024] Wu et al. 2025b] 2024 Liu et al. 2024). For data partitioning, we adopt split ratios of 6:2:2 for the ETT datasets and 7:2:1 1 for the Electricity, Traffic, and Weather datasets. For the few-shot learning task, only 5% or 10% of the original training data is retained, while the validation and testing sets stay the same.
http://pems.der cot.eemms..o...oo
wct. uci edw/mddmmmmmmmmm htt.://wwww....a.e..e.eeeteaaaaa 1201
https: https://ww.bbr. bgc- jena mpg. de/wetter/
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D EXPERIMENTAL SETTINGS
MSH-LLM is implemented using the PyTorch Abrary(Paszker et al. 2019). The number of scales is to 3, and employ ID convolution as the aggregation function. All experimental evaluations carried out on NVIDIA A100 (80GB) and NVIDIA GeForce RTX 3090 GPUs. LLaMA-7B (Touvron) al. 2023) is used as the default base LLM. We use the Adam optimizer (Kingma 2014) with an
initial learning rate selected from {10 3 5 x 10 10-3, 3 10 10-4). Unlike prior works that rely on grid search for hyperparameter tuning, we leverage the Neural Network Intelligence (NNI) toolkit to
automatically optimize the hyperparameters. The specific search space for the hyperparameters is detailed in Table
The source code for MSH-LLM publicly available on GitHub)
Table The search space of hyperparameters.
Parameters
Choise
Batch size
{8. 16, 32, 64, 128, 256)
Number of hyperedges scale
{5. 10, 20, 30, 50)
Number of hyperedges scale
{2. 15, 20)
Number of hyperedges scale
{1. 4. 12}
Number text peotorypes at scale {20, 50, 100, 200, 500, 1000)
Number of text peototypes at scale I
{10, 25, 50, 100, 200, 500).
Number text protorypes at scale
[4, 10, 50, 100)
Aggregation window size scale
(2. 4,84
Aggregation window
(2,
(2. 4, 10, 10)
E EVALUATION METRICS
For long-term time series forecasting and few-shot learning, we employ the Mean Squared Error (MSE) and Mean Absolute Error (MAE) as our evaluation metrics, which can be formulated as follows:
MSE 1
80 H T+ITTH
X T-177H
MAE
1 Xo T+IT+H 4 (10) H
where T and H are the input and output lengths, O results and ground truth, respectively.
T+1TTH and X X41TTH are the forecasting
For short-term time series forecasting and zero-shot learning tasks, the Symmetric Mean Absolute Percentage Error (SMAPE), Overall Weighted Average (OWA), and Mean Absolute Scaled Error (MASE) are employed as evaluation metrics, which are defined as follows:
200 H
XO T XO 4
SMAPE
(11)
X
X
h=1
SMAPE
MASE
OWA 2
(12)
SMAPENalver
MASENalve2
T+LT+H X
MASE H
Σ
T+IT-H
(13)
E
X X
XTILTHH-1
XO
1=1
F FULL RESULTS
To ensure fair comparison, we adhere to the unified experimental settings used in previous works ZZoou et al. 2023a Pan et al. 2024 Jin et al. 2024). The average results represent the mean of
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outcomes across different forecasting scenarios, with the best results bolded and the second-best results underlined. An asterisk (*) indicates that some results do not align with the unified settings, so we reran their official code under the unified conditions and fine-tuned their key hyperparameters.
F.1 LONG-TERM TIME SERIES FORECASTING
Table Summmriize the full results of long-term time series forecasting, We can observe that MSH- LLM achieves the SOTA results in 54 out of 70 cases across 7 time series datasets. Specifically, on the well-studied Traffic dataset, MSH-LLM achieves an average error reduction of 11 1.54% and 6.71% % across all baselines. On the challenging Weather dataset, MSH-LLM achieves an average error reduction of 12.78% and I 11.26% across all baselines.
Table 8: Full results of long-term time series forecasting. The input length is set to 512, and the forecasting lengths are set to 96, 192, 336, and 720. Smaller values correspond to better performance. Bolded: Best, Underlined: Second best.
30amg
ICCL 30041
(E1 8: 3050
DNea BRS 3004)
(15 2013)
(Nearits 3070
(3' 1 NT14
1AA.l (003)
(CT 8 300%
(MM 707%)
0L842 ALEAS
0226
01062 0.212
0.20
0.3090 238
0.338.304 8:34
14770 0.12
BJT2
8:338
0.345
1555
0.393
Weater
335
0.777 0.30
BC30 XL391
1444 E1355
255 L138
10254 092
0.2250 207
02221 6:44
0.2901200 287
8 0.55 6.119
Avg
0,337
8.354
0:229 0.250
0.31 0.366
0.3302279
0255 0.371
0225 0.36
0.254023 283
0.3888335
01345-3.71
0.300.3000
62566.377
0.3996330
0:331
6:36
711
011
01:57-135 350
142
01.50
8:100 01302
11522 01744
9.15280247
0153 01551
1151 0/764
11750
01090 309
0 0.1966
731
0
8.375
82200-12077
3355 5,300
159 5226
0.377
0 280
0.322 6388
0 0.300.300
(L546/3355
Arg:
01258
(136)
60361
01002
96
0.765
8:310
12%
6177
777
12400 194
335
0.335
8.375
6 5123
0
0:544
1000 5.555
0455 2.26
(US21 0.35
8.363
408 0.291
02140.25
9-47 0.56
0.991 0.59
0.36 0.55
03966111 117
0.500 376
322
0
8:435
11401 14421
C427 DASS
6 0413
04051440 407
0452
0441
03 P415
14421 445
0.990 565
64552500
0330 158
0a 507
440 D457
0.4600425
425
5.42
44225-417
6422-448
(44400440
06
0.313
0.275 03.99
230 6-25
0300 335
0300 201
0355003377 347
3.5
D
0383 041-
6.R 0200
0.3998427 427
0425 6225
0558 255
BA25 0220
(UPN 457
330
06456
155
6456
6,65
0-38 0456
0.477440 400
03870411
0409 16
122
8.55
0.355 0.573
0SS ELTSS
0 307
0.547 5.55
0.55 236
0U226 441
753
8.188
130 D-121
155
505
0.779
0:42 0.700
00999-10 133
C.18
D1550/90
(0)
AVD
900
105
61000 19
04106-115
0.09
0.545
188 0,21>
0.050 205
0.129 2211
01090
0300 8026
33443111
8F162
8.28
0L330
2386 0310
5.5 F000
0377 0L00
334
0.888 534
0.338
0284 0.555
0.576
6-10
R1325 365
F.2 SHORT-TERM TIME SERIES FORECASTING
Table summarizes the full results of short-term time series forecasting. The results on M4 Others are averaged from M4 Weekly, M4 Daily, and M4 Hourly. We can observe that MSH-LLM achieves the SOTA results on almost all datasets. Specifically, MSH-LLM performs slightly better than AutoTimes and S I-IM-LLM (i.e., 1.45% and 3.01% % average SMAPE improvement), outperforming other latest baselines by a large margin (e.g., 6.68% and 8.1%% average SMAPE improvement over Time-LLM and FPT, respectively).
Table 9: Full results of short-term time series forecasting. We follow the protocol of cxisting work (Pnn et al. 2024 and set the input length to twice the output length. Smaller values correspond to better performance. Bolded: Best, Underlined: Second best.
2014
Tim
2011
197
2513
Thendean
12:412
13 1555
15.13
12.47
12:422
13.457
15.7%
CRAA
07504
0.784
11700
0.80
02/1
3.07
12:00
0.30
4.755
0.700
0418
30434
15.101
06352
15.51
12.507
18.303
12:145
283.00
12:155
35.54
CA6
3.501
6.62
0.500
$3780
3311
1.500
1.53
3700
14.00
12
0.50
1730
1285
1220
5.51
1955
0.500
066
0055
4271
1.20
4.00
01.04
0.31
0.834
1.80
3880
130
1
12.03
13.44
13.53
12112
13459
12
13.05
13.9
13:98
CRIA
685
685
406
0090
1874
1251
1.50
0.89
089
0555
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F.3 TIME SERIES CLASSIFICATION
Table TQ summarizes the full results of time series classification. The baseline results are from existing works (Zhou al. 2023a Wu et al. (022). From Table [0] we can observe that MSH-LLM achieves an average accuracy of 75.38% surpassing all baselines including the best baseline FPT (74%) and TimesNet (73.6%).
Table 10: Full results of time series classification. We follow the protocol of existing work (Zhou
et al. 2023a). The results are averaged from 10 subsets of UEA and higher values mean better performance. Bolded: Best, Underline:: Second best. # in the Transformers means the name of
#former.
TEMISS
Traeforow
CNN
ENN
Clascca weriok
Mehees
Toral
TCN
DLisca
362
562
12.7
513
31.
305
31.5
32.7
31.2
25.1
33.5
357
26.11
33.6
20.3
389
31.1
53.
45.3
067
002
613
68.5
65.7
963
61.6
066
$28
66
96.7
60.3
54.7
Riddeniig
82.3
204
196
31.6
325
13.
321
33.
2631
158
Hastea
MLB
71.
164
773
955
156
748
733
337
71.
116
76
15A
754
7531
111
727
13.2
75.6
98.7
978
193
859
96.1
98.9
962
96.
462
PI
913
10.7
132
$73
909
86
13.0
946
18X
751
664
162
19.1
183
75.1
95.5
06.3
90.3
9011
60.1
1611
81
844
166
60.5
91.8
506
653
848
40.8
516
40.
36.7
9
99.
99.4
97
59.6
100
100
190
100
98.8
956
814
100
180
100
686
312
URivcRonas Lieerry
20.1
88.1
85.6
85.6
15.6
13
859
835
853
15
866
83.
88A
621
90.
818
85.9
13.9
944
Norage
71.
18.3
F.4 FEW-SHOT LEARNING
We follow the same protocol of existing work (Pan al., 2024). Table 11 and Table 12 summarize the results of few-shot learning under 10% training data. In the scope of 10% few-shot learning, MSH-LLM achieves SOTA results in almost all cases. Specifically, MSH-LLM achieves an average error reduction of 7.32% and 3.95% compared to LLM4TS methods (i.e., I'IP-LLM and Time-LLM) in MSE and MAE, respectively.
Table T summarizes the average results and full results of few-shot learning under 5% training data. We can observe that MSH-LLM still achieves SOTA results even with fewer training data. Specifically, MSH-LLM achieves an average error reduction of 10.47% and 6. compared to LLM4TS methods (i.e., I'IP-LL.M and Time-LLMM) in MSE and MAF, respectively.
Table 11: Fhw-shot learning results under 10% training data setting. Results are averaged from all forecasting lengths. Bolded: Best, Underlined: Second best. Full results given in Table
Methods
MSH-LLM
JCML 2034)
SPP-LLM
Tim-L1M
20223)
TimenNet
Itileemer
HnSfommer
Assudumnee
Metic
MSE MAA
MAE
MSE MAE
MSE MAE
MSE MAE
MAEMMEE
MSE MAE
MBE MAE
MSE MAE
MSE MAE
MSE MAE
Weathr
0.23
0221
0.21
0.27
0.30
0.22
0.279 0.300
0.324
0318
0.360
0.3000.341
I.367 0.300
0.73
0.196 9.900
01000.255
D 1250000000 99
8.46
0444404400
000000011
0.97
Trific
1.423 n.29
0.010.307
04290
0.300
0.355
D4450 0.305
0.951 0:555
0.563 0.45
1 0,815
0994
0 0.69
ETTal
0,563
0.539
0.755 0.553
0,390
09100
0,509
0.56
L.80
BTTA2
0.502 (1423
04196419
0.4240.441
0.39 9.421
0.99
D415 04451
TL429 0646
(L466 B4475
0-462 0-455
0.9440013
0.458 0.409
BTTW??
0.22
0.2440.333
0.305 0.341
0.243
0.33
0.33603373
0.7660334
0.300 353
0.863 158
0.333
0,647 0.481
1 1.57.0
F.5 ZERO-SHOT LEARNING
To evaluate the generalization ability of MSH-LLM, we perform zero-shot transfer experiments on M3 and M4 datasets. For M4 M3 transfer. we evaluate the models trained on M4 directly on M3. Specifically, for the Yearly. Quarterly, and Monthly subsets of M3, we assign the corresponding M4-trained models based on their sampling frequencies. For the subsets of M3 Others, the M4 Quarterly model is selected to ensure consistency in forecasting horizons. For MB - M4 transfer, similar strategy is adopted: For the Yearly, Quarterly, and Monthly subsets of M4, the corresponding models trained on M3 datasets
used. To handle M4 Weekly, Daily, and Hourly subsets, conduct inference using the M3 Monthly model, which is consistent with the experimental settings of existing methods (Zhou et al. 2023a Liu et al. 2024).
The comprehensive zero-shot learning results are detailed in Table 14 Remarkably, MSH-LLM establishes new state-of-the-art performance, consistently outperforming all competitive baselines
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Table 12: Full results of few-shot learning under 10% training data. The input length is set to 512, and the forecasting lengths are set to 96, 192, 336, and 720. Smaller values correspond to better
performance. Bolded: Best, Underlined: Second best.
Mathods
MBH-LLM
JCME
LIM
3150)
PPT (Naudit 0027)
201. (134)
Trandoossess
PackIST 0C1. 2019
TraeSNei OCTLR 24077)
GCMML 2017
(Nenstt 2002)
Orrkiy 70320
ET
AStAGeuET INvart''S (031)
Maria
MSE MAE
MSE MAB
MSE MAR
MSE MAN
MSB MAA
MSS MAF
MSS MAA
MSF MAE
NSSE MAR
MSB MAA
MSSE MAF
6.286
3.122 0,210
0:3600.213
0.215
0:253
0.165 215
0.230
253
0.234
0.199 0.212
192
0 03M 6:248
0.20 11351
0.308 0.264
0330 0.564
0.393.0 726
02700 357
0.205 01.383
0.250 799
18789-00 7966
0.229 0.133
03700 61 133
Weathr
6:288
0:292 1U383
01322.03
01206-8 7907
8-323
0.405
0.385
0-1881115 151
Arg
0.230 0.267
0.27-0.255
0.398 02275
0.388 0.358
0.242 27279
0.279 0.300
0.3840 0.304
0.318 0.323
0.318 0.369
0.300 0.342
8:348
1199 0.25
0.359:005 858
11
0.310
0:261 0.556
0.411 0.000
0.558
0,338 806
136
8.109 6.363
170
0.740
0:110 778
0 175 0.50
010600 795
0110000 770
0319 361
0. 0.360:0 6/5
0802 0.61
O S74
0.200
122.00 11315
0.332 0.317
0 03317
01353
01241
03399 11425
1053010
0.510
0.022 0664
0.755 ELSSS
0,273
01309
0.196.229
0-491
96
0.266
0.013
0,253
0.46-9 255
0.14 0221
0446 520
1-483 200
02294415 0410
16550 400
1.13 02002
166 0.55
0 YLAIE
Are
720
0.053 0.339
0.296
0.293
0:022
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1.851 000
114-4 EUS10
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0.433 427
01474 111
031300014
0.861 0,438
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1.467
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0.289 6.337
0.766 13333
0,305 344
0225588 0.335
0 375
0.296-3333 343
0.330
0.353
(1863 0.88
0.352 0.366
0.447 0J487
1,542 (1530
Table 13: Full results of few-shot learning under 5% training data. The input length is set to 512 and the forecasting lengths are set to 96, 192, 336, and 720.- :. indicates 5% training data is insufficient to constitute training set. Bolded: Best, Underlined: Second best.
Modaoh
MSRLEM
JCMB.
HLL. 2024)
CheuditS (121)
1..... 2034)
21241
TameeNer OKTLR 20023)
FEDXOCTT UCSML 2122)
Oleustit 21622)
BT Urbkr 20220
Autodemet (Nenral' 2021)
Marric
MSE MAII
MSR MARI
MSB MAR
MAEE MAR:
MIsI MAR
MISI SAAR
MMS: MAT
MISR MAR
AeST MAR
MAR
MAN
8.34
0.7660 250
175 8:300
0:544
0.207 0.253
0:229
0315.03 0.552
0.218 0.255
0,257
192
9:133 8.253
02200 1L211
2.223-02775
02277 EL276
EL284 306
0C230 2777
0.272 9L300
1L280 0.517
0.300003317
0.244 0L331
C2778 ILE33
Wendax
8:209
03302 1133
0L292 0.322
0.299 51521
01321
01294113
11333.00 00302
1.353-0440 03545
00209 02998
Arg
9.347
0.361
2.700 0.267
0.3640330
03033 0.30
0.355 350
0.265 01 300
0.288 0.316
0.309 0.553
0.37-0.30
0.388-.331 0.371
0310 0.355
8.255
1.990.8 0.255
0.3001 147
0.159 8.355
011000020
0.16100 730
0.31511366 945
11500 0:511
0.715 0.644
0 (3 17
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176
0:304
0.235 0.326
0.175 9L371
0.36-0.30
0.779 21594
0.558
0.301 100
01181 3611
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0:50001115
0.315-0300
113670 796
0.55.0.70
1 0778
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0.422
353
01410 252
04140
0.49 0.258
0.33 502
0854 0.692
0.63004211
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0.255 I 881
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622 423
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0.009 0.535
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0.009 0693
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0.518 8.833
0.394 6411
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0.35
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12225 0,00
002050 230
11206/10 200
0:220
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0.336
U.40 04455
0.242
0.344
01254 06322
0.250
03500 100
13500 20
11296 0.49
0.799
0.555
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9.111 0-E34
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D478 650
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8L333
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0311-0.77
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Table 14: Full results of zero-shot learning. We adopt the same protocol of existing work (Pan et al. 2024). M4-M3 means training on M4 datasets and testing on M3 datasets, and vice versa. Lower SMAPE means better performance. Bolded: Best, Underlined: Second best.
Medied
(0umg)
(Nout15 2004
(5a 2035
[AAA13302
(10.8 3323)
(C0J 3323)
(Nc.IP5
[CMBL 2033
(AAA1
(C21.8 3019
Yoafr
12:59
16,43
17.433
15.99)
18:350
11:093
10:303
15:230
10.133
0-400
13:003
13,779
14.30
15.000
14.310
08810)
18:833
14,800
Oitan
3.76
12.487
13
143193
13.743
14.100
13:231
133.33
13:333
13.700
161183
18333
11885
11
18.303
11:311
0.303
2.91
3.106
0381
0.901
0.77
7.005
7380
diverse transfer scenarios. On average, our approach achieves substantial reduction in SMAPE error by over 10.23%. The performance gains are particularly pronounced in challenging tasks such as M4-+M3 Others and M3-+M4 Others, where the average error drops by 23.04% and 14.72%, respectively.
G ABLATION STUDIES
Multi-Scale Extraction (ME) Module. To investigate the effectiveneess of the ME module, we conduct an ablation study by carefully designing the following variant:
-w/o ME: Removing the multi-scale extraction module and only performs alignment between input time series and text prototypes.
Table 15:
The results of different ME modules and hyperedging mechanisms on the ETThl dataset.
The best results are bolded.
Methods
-wo ME
-wlo HM
-PM
MSH-LLM
Metire
MSB MAE
MSE MAE
MSE MAE
MSH MAE
96
0,400
0.693 0,560
0,380 0,392
0.360 0.388
192
0,413 0.412
0.751 0.513
0.405 0.424
0.398 0.411
336
0.421 0.436
0.756 0.596
0.423 0.443
0.415 0.432
The experimental results on the ETThl dataset are shown in Table 13 We can observe that MSH-LLM performs better than -w/o ME, showing the effectiveness of the ME module. The reason is that the ME module can provide richer representations than relying solely on single-scale alignment.
Hyperedging Mechanism. To investigate the effect of the hyperedging mechanism, we conduct an ablation study by carefully designing the following two variants:
-w/o HM: Removing the hyperedging mechanism and directly performing alignment between temporal features and text prototypes at different scales.
-PM: Replacing the hyperedging mechanism with the patching mechanism.
The experimental results on the ETThl dataset are shown in Table [5] We can observe that MSH- LLM performs better than -w/o HM and -PM, demonstrating the effectiveness of our hyperedging mechanism in enhancing the semantic information of time series semantic space. In addition, we can observe that -w/o HM achieves the worst performance, the reason is that the individual time point or temporal feature contains less semantic information, making hard to align with the informative semantic space of natural language.
Multi-Scale Text Prototypes Extraction. To investigate the impact of different multi-scale text prototypes extraction, conduct ablation study by designing the following two variants:
R.I: Replacing word token embeddings based on pre-trained LLMs with word token embeddings generated from manually selected word and phrase deseriptions (e.g., small, big, rapid increase, and steady decrease).
R.2: Replacing word token embeddings based pre-trained LLMs with word token embeddings generated from randomly selected word and phrase descriptions (c.g., increase, happy, can, and white noise).
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Table 16: The results of different multi-scale text prototypes extraction and CMA modules the ETThl dataset. The best results are bolded.
Methods
R.1
R.2
R.3
P.I
-ASO
MSH-LLM
Metric
MSE MAE
MSE MAE
MSE MAE
MSE MAE
MSE MAE
MSE MAE
96
0.467 0.467
0.441 0.447
0.697 0,561
0.363 0,390
0,396 0.413
0.360 0,388
192
0.497 0.483
0.475 0.467
0.760 0.600
0,405 0.417
0.417 0.428
0.398 0.411
336
0,517 0.496
0.514 0.489
0.787 0.609
0.417 0.424
0.433 0.442
0.415 0.432
The experimental results on the ETThl dataset are shown in Table [6] from which we can observe that MSH-LLM performs better than R. and R.2 by a large margin, which indicates the effectiveness of our multi-scale text prototype extraction over approaches of manually selecting, In addition, notable that we initially assumed that aligning multi-scale temporal features with relevant natural language descriptions (e.g., small, big. rapid increase, and steady decrease) can offer better performance. However, the experimental results show that word token embeddings generated from randomly selected word and phrase descriptions achieve better performance than R.1. The reason is that the aligned word token embeddings may not be fully related to time series. Actually, LLMs can function pattern recognition machines (Sun al.) 2024 Zhou al. 2023a), and believe the text prototypes matched by LLMs can better match temporal patterns, even if they may not be fully related time series.
To investigate the effectiveness of the cross-modality alignment module, we conduct an ablation study by designing the following two variants:
CMA Module.
R.3: Removing the CMA module and directly concatenating the hyperedge features with MoP before feeding them into LLMs to obtain the output representations.
P.I: Performing detailed cross-modality aligmment across all scales.
The experimental results on the ETThl dataset are shown in Table 16. from which we can observe that MSH-LLM performs significantly better than R.3, showing the elfectiveness of the CMA module. The reason that the CMA module can help align the semantic space of natural language and that of time series. In addition, we can observe that MSH-LLM outperforms P.1 in most cases. This because performing detailed alignment across all scales may introduce redundant information interference.
In addition, has been shown that treating cross-modality alignment as an independent task (Li et al. 2023) can help the model focus more on the alignment objective and may potentially improve model performance. To investigate the impact of different cross-modality aligmment strategies, we conduct ablation studies on the ETTh1 dataset by carefully designing the following variant:
-SSO: This approach treats cross- cross-modality alignment as a standalone objective and employs two- stage training strategy for time series analysis. The detailed design of the objective function is formulated as follows:
Specifically, for the given hyperedge feature et and text prototypes u. scale first compute both the cosine similarity and the Euclidean distance between them. 'The cosine similarity can be formulated as follows:
(e5)"
Ts
(14)
| ll, |e>
where |1-112 represents the L2 norm. The Euclidean distance can be defined as:
Di.5 = |ee - -iil2 E² ((e))d (4))
(15)
Then, the loss function Te at scale s based on the correlation weight and Euclidean distance can be formulated as follows:
M Σ M
L L
Dis + (1 Tt.j )mar( Da.j. 0)).
(16)
(M*)²
where 7 > denotes the threshold. Notably, when Tı,j 1, indicating that of and ut are deemed M1 MM
similar, the loss turns to Lass
[M.]² En Ej=1 Ti.j Digi where the loss will increase if D
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Published as a conference paper at ICLR 2026
becomes large. Conversely, when dij 0, meaning e; and e₁ regarded as dissimilar, the loss turns to Laso (ar-)² Σi=1 D Tr.))mar(( - Di.j 0), where the loss will increase if Da,j falls below the threshold and turns smaller. Other cases lie between the above circumstances. The final loss function can be formulated as follows:
L Σ
L
as00
(17)
The experimental results are shown in Table 16 than -ASO in most cases, We attribute the performance drop to the following two aspects: 1) Treating cross-modality alignment as a standalone objective, the model may lack supervision signals from the primary time series analysis task, thereby missing the potential synergy with the main task. 2) Unlike CV NLP, time series datasets often contain limited training samples, which may result in insufficient generalization capability when cross- modality alignment is trained independently as standalone objective. The experimental results show the effectiveness of our CMA module.
We can observe that MSH-LLM performs better
LLM Backbones. To investigate the effectiveness of LLM backbones for time series analysis, we conduct an ablation study by designing the following two variants:
-w/o LLM: Removing the LLM backbones and directly feeding the connected multi-scale temporal features into the linear mapping layer.
-LLM2Attn: Replacing the LLM backbones with single multi-head attention layer.
Table 17: The results of LLM backbone variants on the ETTh1 dataset.
The best results are bolded.
Methods
-w/o LLM
-LLM2Attn
MSH-LLM
Metric
MSE MAE
MSE MAE
MSE MAE
96
0.401 0.437
0.381 0.405
0.360 0.388
192
0.435 0.447
0.415 0.423
0.398 0.411
336
0.441 0.453
0.421 0.437
0.415 0.432
The experimental results on the ETThl dataset are shown in Table 17 from which we can observe that MSH-LLM performs better than -woo LLM and -LLM2Attn, demonstrating the effectiveness of LLM backbones for time series analysis.
H VISUALIZATION
Visualization of the Hyperedge Embeddings. We perform qualitative analysis to investigate the training-time trajectories of the hyperedge embeddings. The t-SNE visualization results of hyperedge embeddings on the ETThl dataset are given in F From Figure 7 we can discern the following tendencies: 1) As training progresses, hyperedge embeddings at different scales form distinct clusters. This indicates that MSH-LLM is able to distinguish and capture multi-scale temporal patterns, In addition, even within the same scale, different hyperedge embeddings reside in distinct clusters, indicating the ability of MSH-LLM in capturing diverse temporal patterns within the same scale. 2) From Figure a) to Figure c), we can observe that embeddings of large-scal, scale hyperedges form distinct clusters earlier during training, while embeddings of small-scale hyperedges gradually separate from the large-scale clusters over time. This suggests that during the early stages of training, the model is more focused capturing coarse-grained temporal patterns (c.g., weekly patterns), and later shifts its focus to learning finer-graine grained temporal patterns(c. g.. hourly and daily patterns).
I MODEL ANALYSIS
I.1 GENERALITY ANALYSIS ON DIFFERENT LLM BACKBONES.
For a fair comparison, following existing works (Lu et al. 2024 Pan et al. 2024), we use LLaMA-7B the default LLM backbone. However, MSH-LLM designed to enhance the general ability of LLMs to understand and process time series data, rather than being tailored to specific LLMs (e.g.. ILLMA-7B). To evaluate the performance and generality of existing methods, evaluate MSH-LLM
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(a) Inital enbedling
(b) Enitekkings epoch
(c) Enkedlingg spoch
Figure 7: The t-SNE visualization of hyperedge embeddings different epochs.
with other baseline methods on more advanced LLMs. We adopt LLaMA-3. 1-BB (Grttatiioi et al. 2024) (-w L-8B), Qwen2.5-7B (Yang al., 2024a) (-w Q-7B), and DeepSeek DeepSeek-RI-Diidgg RI-Distil--LaMMA (Guo et al. 2025) (-w D-8B) for comparison. The experimental results on the ETThl dataset with input length T=512 and output length H=96 are presented in Table T8
Table 18: The results of different LLM backbones the ETTh1 dataset. The best results are bolded.
Methods
L-SB
Q-78
D-SB
LLaMA-7B (Default)
MAE
S-IP-LLM
0.350 0.393
0.364 0.395
0,362 0. 0.395
0.366 0. 396
Time- LLM
0.378 0.403
0.413
0.378 0.408
0.383 0.410
MSH-LLM
0.350
From Table 18
we can observe that existing LLM4TS methods (i.e., MSH-LLM SiIP-LLM, and advanced LLMs, demonstrating the significance of
Time-LLM) achieve better performance the choice of LLM backbones for time series analysis. In addition, we can observe that MSH-LLM shows a more significant improvement compared to other methods when using more advanced LLM backbones. This indicates the effectiveness of the framework design, rather than being merely influenced by the LLM backbones.
1.2 ROBUSTNESS ANALYSIS
All experimental results reported in the main text and appendix are averaged over three runs with different random seeds. To evaluate the robustness of MSH-LLM to the choice random seeds, we report the standard deviation of MSH-LLM under long-term time series forecasting settings. The experimental results are shown in Table 19 and 20 We can observe that the variances are considerably small, which indicates the robustness of MSH-LLM against the choice of random seeds.
Table 19: The standard deviation results of MSH-LLM on Weather, Electricity, and Traffic datasets. Results are averaged from three random seeds.
Dataset
Weather
Electricity
Traflic
Horizon
MSE MAE
MSE MAE
MSE MAE
0,13810.0005 0.0000000000
0.127:0.0012 0.201-10.0005
0.365.10.0000 0.200
192
0.18720.0010 0.230=0..0099
0.100:00.0006 0,242:00.0003
0.572:10.0005 0.281=0.0000
336
0.257 10.0077 0.200.
0.162.10.000 10.0000 0,258
0.385.10.0000 0.209
0.198:20.0005
0.40210.0006
In addition, it is notable that achieving significant performance improvement across all well-studied datasets is inherently challenging. To rule out the influence experimental errors, instead of just showing the MSE and MAE results, repeat all experiments 3 times and report the standard deviation and statistical significance level (T-test) of MSH-LLM and the second-best baseline (i.c., S'IP-LLM). The experimental results shown in Table 21
From Table 21 we can observe that the statistical significance reaches 95%, indicating that the performance improvements achieved by MSH-LLM substantial and consistent across all datasets.
To evaluate the robustness of the proposed method, we compare MSH-LLM with baselines (i.e., I-IP-LL,, Time-ILM, and FPT) across three challenging scenarios: forecasting with anomaly
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Table 20: The standard deviation results of MSH-LLM the ETT dataset. Results averaged
from three random seeds.
Dataset
ETThi
ETTh2
ETTml
ETTm2
Horizon
MSE MAE
MSE MAE
MSE MAE
MSE MAE
96
0.360+-0.0007 0.388 0.388-0.0005
0.273 40.004
0.285-+0.0031 0,340+-00001
0.161+000000 0.246-00.000
192
0.39810000014 0.000
0.335-0.0005 0.302
0.013 0.358.10.0017
0.21810.00088 0.284
336
0.415=0.0010 0.432=0.0007
0.363=0.0007 0,400=0.0000
0.355=0.0068 0.37740000024
0.271=0.0005 0.320=0.0003
720
0.436.00.0003 0.447:00.0006
0.396_000015 0.428.000009
0.4051000021 0.410.10.0062
0.35810.00077 0.392..0.0044
Table 21: The standard deviation and T-test results of MSH-LLM and the second-best baseline. Results are averaged from three random seeds.
Dutaset
MSH-LLM
S'IP-LLM
Confidence Interval
Horizon
MSE MAE
MSE MAE
Percent
96
0.217= 0.2005
0.223++0.0007 0.0095
99%
192
0.159=0.0006 0.253=0.0003
0.163 0006 0.008::000005
99%
336
0.381=0.0003 0.283=0.0004
0.406=10.000 0.287=:0.0004
99%
720
0.334+0.0020 01371
0.338++0.0014 0.379++0.0010
95%
injection, ultra-long forecasting, and forecasting with missing data. The corresponding results are presented below. Note that to quantify robustness, compute the performance drop rate (PDR) as:
r-
PDR
(18)
T
where I' and I' are forecasting results and forecasting results under challenging scenarios, respectively. Higher PDR values indicate lower robustness. The reported PDR is averaged across the MSE and MAE metrics to provide comprehensive evaluation.
Forecasting With Anomaly Injection. We conduct experiments by injecting randomly generated anomalies in the training data. The anomaly rate varies from 10% to 20%. The experiments conducted on the E dataset with the input length set to 512 and output length set to 96. Table summarizes the results of forecasting with anomaly injection.
Table 22: Forecasting results with anomaly injection on the ETThi dataset. The best results bolded.
Methods
MSH-LLM
S'IP-LLM
Time-LLM
FPT
Metric
MSB MAE PDR
MSE MAE PDR
MSE MAE PDR
MSB MAE PDR
0%
0.360 0,388
0,366 0,396
0.383 0.410/
0.402 /
10%
0.374 0.393 2,589
0.712 69.743
0.398 0,419 3.056
0.393 2.970
15%
0.425 0.427 14.053
0.723 0.578 71.750
0.443 0.435 10.812
0.741 1.103 134.946 0.935 1.421 200.092
20%
0.773 0.598 81.106
0.773 0.598 81.106
0.751 0.589 69.871
From Table 22 we can obtain the following tendencies: 1) MSH-LLM achieves the best performance in almost all cases, showing its superior ability time series forecasting even under scenarios with anomaly injection. 2) Although the performance of all methods declines as the anomaly ratio increases, MSH-LLM exhibits slower performance degradation compared to the other methods, demonstrating its robustness for forecasting with anomaly injection. 3) When the anomaly ratio reaches about 20%, the PDR value of MSH-LLM is greater than 20%, indicating that the robustness boundary of MSH-LLM is near 20% anomaly injection.
Ultra-Long- Term Forecasting. We conduct ultra-long-term time series forecasting by taking fixed input length (T=512) to predict ultra-long horizons (H=(1008, 1440, 1800)). Table 23 summarizes the results of ultra-long-term time series forecasting.
we can observe that MSH-LLM achieves SOTA results almost all cases, showing the effectiveness of MSH-LLM for ultra- Aitra-long-tem time series forecasting. In addition, although all baselines suffer from performance drops when increasing forecasting horizons, MSH-LLM declines more gradually. The reason may be that the multi-scale hypergraph structure enhances the ability of L.MMs in understanding and processing ultra-long-term time series.
From Table 23
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Table 23: Ultra-long-term forecasting on the ETTh1 dataset. The best results are bolded.
Methods
MSH-LLM
S'IP-LLM
Time-LLM
FPT
Metric
MSE MAE
MSE MAE
MSE MAE
MSE MAE
1008
0.463 0,498
0.543 0.520
0.478 0.475
0.527 0,576
1440
0.516 0.513
0,806 0.642
0.547 0,521
0.594 0.716
1800
0.648 0.557
0,940 0.725
0,683 0.5587
0.660 0.886
Forecasting With Missing Data. We conduct forecasting with missing data by randomly masking the training data. The experiments are conducted on the Electricity dataset with the input length set to 512 and output length set to 96. Table 24 summarizes the results of forecasting with missing data.
Table 24: Ultra-long-ter forecasting on the ETTh1 dataset. The best results are bolded.
Methods
MSH-LLM
S-IP-LLM
Time Line-LLM
FPT
Metric
MSE MAE
MSE MAE
MSE MAE
MSE MAE
0%
0.360 0.388 /
0,366 0,396/
0,383 0.410/
0.379 0,402/
5%
0.368 0.3.93 3.511
0.385 0.403 3.479
0,392 0.416 1.907
0.392 0.431 5.307
10%
0.409 0.421 11,058
0.432 0,447 15.456
0.451 0.449 13.633
0.478 0.483 22.704
From Table 24, we can obtain the following tendencies: 1) Existing L.MM-based methods show little performance degradation with 5% missing data. The reason may be that LLM4TS methods can leverage transferable knowledge learned from large-scale corpora of sequences, thereby enhancing their abilities in understanding and reasoming time series. 2) MSH-LLM performs better than other LLM4TS methods, the reason is that the hyperedging mechanism can capture group-wise interactions, which increase the robustness of LLM in forecasting with missing data. 3) When the missing data ratio reaches about 10%, the PDR value of MSH-LLM is greater than 10%, indicating that the robustness boundary of MSH-LLM is near 10% missing data.
Table 25: Results compared with simple methods on the ETThl dataset. The best results are bolded.
Methods
DHR-ARIMA
Repeat
PAtmn
MSH-LLM
Metric
MSF MAF
MSE MAE
MSE MAE
MSE MAE
96
0.894 0.613
0.713
0.383 0.411
0.360 0.388
192
0.872 0.624
1.325 0.733
0.429 0.438
0.398 0.411
336
0,957 0.638
1,330 0.746
0.425 0.443
0.415 0.432
1.3. BROADER BENCHMARK COMPARISON
Comparison With Other Cross-Modality Alignment (CMA) Method. It is known that TimeCMA (Liu et al. 2025a) also uses the CMA mechanism. We need to clarify that despite the shared nomenclature, the implementation and operational mechanisms of the cross-modality aligmment (CMA) modules in MSH-LLM and TimeCMA are fundamentally different. Specifically, the CMA mechanism in TimeCMA operates primarily as retrieval mechanism. Its goal is to query and extract time-scries-relater representations from set of predefined, hand-crafted textual prompts. This process can be seen as form of feature selection, where the most relevant linguistic cues are retrieved to augment the time series embeddings. In contrast, the CMA module in MSH-LLM operates primarily as an alignment and fusion mechanism. It is designed to align the multi-scale hyperedge features with multi-scale text prototypes generated from the token embeddings of LLMs. This process aims to align the modality between natural language and time series. In addition, we have included TimCMA for comparison. Note that due to its fixed prompt templates and restrictions on LLM selection, failed to rerun TimeCMA under the unified settings. For fair comparison, we reran MSH-LLM using the same settings as TimCMA. The experimental results on the ETThl dataset with the input length T=96 shown in Table 26
From Table 26 we can observe that MSH-LLM performs better than TimeCMA in almost all cases, demonstrating the effectiveness of CMA mechanism used in MSH-LLM.
Comparison With Simple Methods. Recent studies have questioned the effectiveness of previous LLM-based methods for time series analysis (Tan al. 2024). Some studies even show that
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Table 26: Results compared with simple methods the ETTh1 dataset. The best results are bolded.
Methods
TimeCMA
MSH-LLM
Metric
MSE MAE
MSE MAE
0.373
01393
192
0.427 0.421
0.417 0.416
0.458 0.448
simple attention layer non-neural methods (Hewamalage et al. 2023 can achieve comparable performance. To further evaluate the performance of MSH-LLM against simple methods, we add three simple methods, i.e., PAtnn (Tan et al.) 2024), DHR-ARIMA (Hewamalage et al.) 2023). and Repeat (used in DLinear (Zeng et al. 2022) for comparison. All experiments are run under unified settings. The experimental results on the ETThl dataset are shown in Table 25
we can observe that MSH-LLM performs better than simple methods in most cases. Specifically, MSH-LLM reduces the MSE errors by 56.89% 70.31 %, and 5.20% compared to DHR- ARIMA, Repeat, and PAtnn, respectively. The experimental results demonstrate the effectiveness of MSH-LLM over simple methods.
From Table 25
Here, we attribute the limited effectiveness of previous LLM-based methods for time series analysis to three key factors: Firstly, the semantic spaces of natural language and time series are inherently different. Existing methods (e.g.. FPT (Zhou et al. 2023a)) directly leverage off-the-shelf L.MSs for time series analysis without proper alignment, making difficult for L.MS to understand and process temporal features. Secondly, we found that some of these methods (e.g.. CALF (Liu al. 2025b) and FPT) do not even use prompts for LLMs, despite prompts being proven crucial for activating the reasoning capabilities of LLMs. The third and most important factor is that existing LLM-based methods directly segment the input time series into patches and feed them into LLMs. However, simple partitioning of patches may introduce noise interference and negatively impact the ability of L.MSs to understand and process temporal information.
In contrast, our proposed method incorporate hyperedging mechanism, CMA module, and MoP mechanism, all of which are designed to better align LLMs for time series analysis. Ablation studies in Section [.6 and Appendix confirm that these components can enhance the ability of LLMs to understand and process temporal information. Experimental results in Appendix [.2 further validate the effectiveness of our method in both utilizing LLMs and addressing concerns about the performance ceiling of previous methods.
J PROOF
In our numerical experiments and visualization analysis, we find that different hyperedge representa- tions capture distinct semantic information and can enhance the ability of LLMs in reasoning time series data. To further explore, we use the following theorem to characterize this behavior.
Theorem 1 (Informal). Consider the self-attention mechanism for the I-th query token. Assume that the input tokens X; (i = 1,2,..., n. have bounded mean μa. Under mild conditions, with high probability, the output value token X, with high probability converges to μW; at rate of OO where W, is the parameter matrix used compute the value token.
This indicates that the self at-etttenion mechanism used in LLMs can efficiently converge the output token representations to a stable mean (i.e., the representative semantic center). For time series analysis, if there are translation-invarian self-attention can help identify those invariant structures more effectively by comparing given token with others. This phenomenon is especially important in few-shot forecasting high-noise scenarios as it helps avoid overfitting to noise and improves generalization.
structures or patterns (e.g., periodicity and trend), the
However, raw time series data suffer from two main limitations: 1) Individual time points contain limited semantic information, making it difficult to reflect structural patterns (e.g., periodicity and trend). 2) The raw sequence is often corrupted by noise, resulting in a low signal-to-noise ratio. To address these issues, we introduce multi-scale hypergraph structures, which adaptively connect multiple time points through learnable hyperedges different scales. This method can enhance the
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multi-scale semantic information of time series while reducing irrelevant information interference. It provides the self-attention mechanism in LLMs with more structured input, enabling self aelf-attention to distinguish between temporal patterns and noise. As result, the generalization and robustness of LLMs are improved.
We denote the i-th element of vector X Tt, the element in the i-th row and j-th column of matrix W as Waj. and the j-th row of matrix W as Wj.- Furthermore, we denote the i-th hyperedge representation (token) of the input as Xx, where x; X, Following existing work (Zhou al.) 2023a), before giving the formal statement of Theorem E.I, we first show the following three assumptions.
1. Each token x, is a sub-Gaussian random vector with mean μn and covariance matrix (o²/d)L for i 1.2....,.2.
2. The mean vector follows a discrete distribution over a finite set V. Furthermore, there exist constants 0 < V1 and < M2 1/4 such that:
a) | =11 V1,
b) H Wo WTH: E [12, vi] for all i, and le We W Wk S v2 for all * #y E V.
3. The matrices Wy and W Q W are element-wise bounded by V/5 and 16. respectively. That is. [[Wv]el v3 and [ W klal S be for all i.j E [d].
In the above assumptions, we ensure that for a given query hyperedge representation, the difference between the clustering center and noises are large enough to be distinguished. Then, we give the formal statement of Theorem as follows:
Theorem 2 (formal statement of Theorem 1). Let each hyperedge representation x; be a or- subgaussian random vector with mean H4, and suppose all n hyperedge representations share the same query cluster center. Under the aforementioned assumptions, if v1 > 3(v(6, d) + 1/2 + v4). then with probability at least 1 - 56, we have:
E exp (12x: W ( W. W xxT)
14 Wr
Ej= exp
(axxx
((6,d)
2d
< 4exp
Vd
0V/ V dn
log
5
1/2 144 + =b(
+ 7 exp
1
|m (u:Wvlll Wvlloc:
Vd
where (65 d) 201146 v 2og (t) + 20 log (1)
Proof. See the proof of Lemma in (Wang al. 2022 with k n.
K LIMITATIONS AND FUTURE WORK
In the future, we will extend our work in the following directions. Firstly, due to our CMA module performing multi-scale alignment a fully learnable manner, is interesting to introduce a constraint mechanism to further enhance the alignment between multi-scale temporal features and multi-scale text prototypes. Secondly, compared to natural language processing and computer vision, time series analysis has access fewer datasets, which may limit the expressive power of the models. Therefore, in the future, we plan to compile larger datasets to validate the generalization capabilities of our models more extensive data.
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L USE OF LLMS
The authors use LLM solely as a general-purpose assistive tool for grammar and format refine- ment. LLM does not contribute to research ideation experimental design. The authors take full responsibility for the content of this paper.
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Reviews:
[{"review_id": "ovZjLb5IQM", "reviewer": "Reviewer_iwbv", "summary": "The paper addresses the problem of multi-scale semantic misalignment between large language models (LLMs) and time series data by proposing MSH-LLM, a cross-modal alignment framework based on multi-scale hypergraphs. The core innovation lies in the hyperedge mechanism, which enhances the multi-scale semantic representation of time series, together with a hybrid prompting strategy that activates the temporal reasoning capability.", "questions": "1. Please include training-time trajectories of the hyperedge embeddings E_hyper^s. An analysis that maps dominant embedding directions to known multi-scale patterns in the data, for example, daily and weekly cycles in ETT, could substantially improve the interpretability of the hyperedging mechanism.\n\n2.  Please evaluate the robustness of MSH-LLM under extreme conditions. Specifically, include results on ETTh1 subsets with 10% to 20% injected anomalies, ultra-long forecasting with H=1008, and Electricity subsets with 5% missing data. A comprehensive discussion of how the model behaves across these challenging scenarios—and particularly where performance begins to degrade—would provide valuable insight into the robustness boundaries of MSH-LLM.\n\n3.  Please provide inference latency and memory usage on an NVIDIA A100. For example, report single-pass inference time on ETTh1 with H=720and the peak GPU memory under batch size =32. These measurements are important to assess real-time feasibility.\n\n4.  Please run targeted ablations that remove only the “logical reasoning” prompt or only the “time-series reasoning” prompt, and report the resulting performance changes. This would help determine whether the two components are redundant and if they can be merged to simplify the prompt design.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper insightfully identifies the multi-scale structural misalignment between natural language and time as a fundamental bottleneck for applying LLMs to time-series analysis.\n\n2. By introducing learnable hyperedges and a sparsification strategy, the proposed method not only mitigates the noise sensitivity of traditional patch-based approaches but also captures implicit group-level interactions within time series data.", "weaknesses": "1. The paper does not justify the node–hyperedge similarity in the hyperedging mechanism (Eqs. 2–3), nor does it analyze the theoretical relation between the number of hyperedges M^s and the time-series dimensionality D.\n\n2. The work does not report inference latency on ETTh1 with H=720, so it is unclear whether the method is suitable for real-time scenarios such as power-load dispatch. Memory consumption is also missing; GPU usage on high-dimensional data is not reported, limiting an assessment of deployability.\n\n3. Within the capability-enhancing prompts, “logical reasoning appears to overlap with 'time-series reasoning, but there is no ablation to test redundancy.", "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": "yNIWCoSSGZ", "reviewer": "Reviewer_2He4", "summary": "To address the modality alignment between natural language and time series in LLM-based time series analysis, this paper focuses on multi-scale structures by proposing a novel multi-scale hypergraph method, MSH-LLM. This method integrates multi-scale extraction, hyperedging, cross-modality alignment and mixture of prompts mechanisms to enhance the multi-scale semantic information in LLM-based analysis. Experimental results demonstrate the effectiveness of MSH-LLM.", "questions": "See weakness part for details.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper introduces a hyperedging mechanism to extract group-wise information from multi-scale temporal features, enhancing multi-scale semantic information of time series semantic space as a novel contribution.\n\n2. This paper designs a cross-modality alignment module to perform multi-scale alignment and obtain richer representations, while the mixture of prompts mechanism enhances the reasoning ability of LLMs.\n\n3. This paper conducts comprehensive experiments on 27 real-world datasets across 5 different applications, demonstrating the effectiveness of MSH-LLM.", "weaknesses": "1. In Sec 4.1, the word token embeddings U are transformed into U1 through linear mapping. Is the linear mapping learnable or a semantic-distance-based mapping? Will each text prototype possesses explicit meaning? \n\n2. As the pre-trained LLM is freezed during training according to Figure 1, I am confused whether all the proposed modules will be trained simultaneously. If so, what are the training objectives? \n\n3. Experimental results in Table 1 and 2 show minor improvements between MSH-LLM and the runner-up methods. As the base LLM is the early LLaMa-7B, it is hard to judge whether the performance gap come from the LLM backbones or the method itself.", "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": "4S9JkduYqk", "reviewer": "Reviewer_GvBV", "summary": "This paper presents a Multi-Scale Hypergraph method that aligns large language models for time series analysis. A hyperedging mechanism is designed to enhance the multi-scale semantic information of the time series semantic space, while a mixture of prompts mechanism is introduced to provide contextual information. Extensive experimental results on real-world datasets demonstrate the superior performance of the proposed method.", "questions": "- Could the authors clarify how their proposed hyperedging mechanism differs from prior hypergraph-based models such as MSHyper or Ada-MSHyper in terms of functionality?\n\n- Since the CMA module is conceptually similar to that in TimeCMA [2], could the authors explain what improvements are brought by its integration in this work?\n\n- Each file in the code repository link cannot be opened successfully. Could the authors ensure reproducibility by providing a working link or additional implementation details?\n\n- The paper claims novelty in combining multi-scale structures and LLMs. Could the authors discuss whether this combination provides theoretical advantages beyond empirical gains, and how generalizable it is to other LLM backbones?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper introduces a multi-scale hypergraph framework to align LLMs for time series analysis. The proposed hyperedging mechanism and mixture of prompts strategy demonstrate better performance beyond existing single-scale or prompt-based methods.\n- This paper is well-written and easy to follow.\n- The framework is technically solid and systematically evaluated on 27 real-world datasets covering forecasting, classification, few-shot, and zero-shot learning.", "weaknesses": "- The provided code files could not be opened successfully, which raises concerns about the reliability of the reported experimental results.\n\n- The overall framework appears to combine elements from Time-LLM [1] and hypergraph-based multi-scale modeling. As a result, the methodological novelty may be limited unless the authors can clearly articulate the unique contributions beyond this integration.\n\n- The Cross-Modality Alignment (CMA) module used for aligning text and time-series modalities has already been proposed in prior work [2]. Since this paper does not cite that reference, it is unclear whether the presented CMA design introduces any substantive technical advancement.\n\n[1] Time-LLM: Time Series Forecasting by Reprogramming Large Language Models. ICLR 2024.\n\n[2] TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment. AAAI 2025, 39(18): 18780–18788.", "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": "R4WzhFqL6X", "reviewer": "Reviewer_GFpP", "summary": "This paper proposes MindTS, a multimodal time-series anomaly detection framework that leverages fine-grained alignment between time-series segments and textual signals from two sources: endogenous (template-guided descriptions generated per segment) and exogenous (external background texts). The method first aligns time–text features via cross-view attention and contrastive objectives, then performs a content condensation step (information-bottleneck-style masking) to reduce textual redundancy, and finally conducts cross-modal reconstruction with masked time-series to compute anomaly scores. Experiments over six real-world datasets and 17 baselines show consistently strong or SOTA performance, with ablations validating the necessity and ordering of the key modules.", "questions": "1. It is better to train the multimodal alignment as a standalone objective (rather than only as an auxiliary loss) and report a dedicated set of results—ideally comparing standalone vs. auxiliary alignment settings.\n2. It is better to provide a ablation study on the design of endogenous prompts (e.g., template variants, statistical descriptors, temporal granularity), to quantify their impact on the final performance.\n3. The framework appears extensible to broader multimodal time-series tasks (e.g., classification and forecasting). It is better to evaluate on a few forecasting datasets, or more narrowly, on forecasting-based anomaly detection (early-warning) to assess transferability.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The design of each modular ( alignment → condensation → cross-modal reconstruction ) is well-motivated and confirmed by ablation studies.\n2. The empirical coverage is broad: 6 datasets, 17 baselines, plus multimodalized baselines,  with consistent gains and careful sensitivity studies.\n3. It provides insightful guidance on the coordinated use of endogenous and exogenous texts in alignment with time-series signals, and the proposed method for assessing and condensing informative content is compelling.\n4. The exposition is well-structured and logically coherent, with rigorous reasoning throughout.", "weaknesses": "1. Limited depth in optimizing multimodal alignment. Multimodal alignment somtimes trained as a standalone objective (e.g., BLIP2), whereas here it is treated as an auxiliary task, which may constrain the depth of alignment achieved. Although Figure 3 reports preliminary component ablations, a more systematic study would substantiate the alignment claims.\n2. Insufficient ablation on endogenous prompt design. The construction of endogenous prompts appears to substantially affect performance in time-series contexts (cf. TimeLLM, HiTime). Some controlled ablations over prompt templates, statistical descriptors, and temporal granularity would clarify robustness and inform 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": {}}]

Author rebuttal:

Decision: Accept (Poster)