Unlocking the Value of Text: Event-Driven Reasoning and Multi-Level Alignment for Time Series Forecasting
Authors: Siyuan Wang
Peng Chen
Yihang Wang
Wanghui Qiu
Chenjuan Guo
Bin Yang
Yang Shu
Venue: ICLR 2026 Poster (2026)
Keywords: time series forecasting
multimodal
Primary area: learning on time series and dynamical systems

Abstract: Existing time series forecasting methods primarily rely on the numerical data itself. However, real-world time series exhibit complex patterns associated with multimodal information, making them difficult to predict with numerical data alone. While several multimodal time series forecasting methods have emerged, they either utilize text with limited supplementary information or focus merely on representation extraction, extracting minimal textual information for forecasting. To unlock the Value of Text, we propose VoT, a method with Event-driven Reasoning and Multi-level Alignment. Event-driven Reasoning combines the rich information in exogenous text with the powerful reasoning capabilities of LLMs for time series forecasting. To guide the LLMs in effective reasoning, we propose the Historical In-context Learning that retrieves and applies historical examples as in-context guidance. To maximize the utilization of text, we propose Multi-level Alignment. At the representation level, we utilize the Endogenous Text Alignment to integrate the endogenous text information with the time series. At the prediction level, we design the Adaptive Frequency Fusion to fuse the frequency components of event-driven prediction and numerical prediction to achieve complementary advantages. Experiments on real-world datasets across 10 domains demonstrate significant improvements over existing methods, validating the effectiveness of our approach in the utilization of text. The code is made available at https://github.com/decisionintelligence/VoT.

Published as a conference paper at ICLR 2026
UNLOCKING THE VALUE OF TEXT: EVENT-DRIVEN REASONING AND MULTI-LEVEL ALIGNMENT FOR TIME SERIES FORECASTING
Siyuan Wang, Peng Chen, Yihang Wang, Wanghui Qiu, Chenjuan Guo, Bin Yang, Yang Shu East China Normal University faywang, pchen, yhwang, onehui (archhui@ssuu. @stu.. ecnu. edu. cn, byang, yshu ecnu. edu.cn
ABSTRACT
Existing time series forecasting methods primarily rely on the numerical data it- self. However, real-world time series exhibit complex patterns associated with multimodal information, making them difficult to predict with numerical data alone. While several multimodal time series forecasting methods have emerged, they either utilize text with limited supplementary information or focus merely representation extraction, extracting minimal textual information for forecast- ing. To unlock the Value of Text, we propose VoT, a method with Exvnnt-dried Reasoning and Multi-level Alignment. Event-driven Reasoning combines the rich information in exogenous text with the powerful reasoning capabilities of LLMs for time series forecasting. To guide the LLMs in effective reasoning, we propose the Historical In-context Learning that retrieves and applies historical examples as in-context guidance. To maximize the utilization of text, we propose Multi-level Alignment. At the representation level, we utilize the Endogenous Text Align- ment to integrate the endogenous text information with the time series. At the prediction level, we design the Adaptive Frequency Fusion to fuse the frequency components of event-driven prediction and numerical prediction to achieve com- plementary advantages. Experiments real-world datasets across 10 domains demonstrate significant improvements over existing methods, validating the ef- fectiveness of our approach in the utilization of text. The code is made available at https//giiibbbcmmmcccinnnnnnnnne
INTRODUCTION
Time series forecasting a funda- mental task in numerous domains, in- cluding financial market analysis, cli- mate monitoring (Tian et al. 2025),
An economic
Housing market crisis. (2008. 5.26)
COVID-19 (2020.1.4
Stay home (20920..199)
enash. (2007
and healthcare management, traffic awareness (Guo et al. 2014). and spatio-temporal tracking (Lu et al. 2011). Deep learning-based fore- casting methods have achieved com- petitive performance (Nie et al.
2023 Chen et al. 2024 Qiu et al.
Figure I: Unemployment rate time series (Liu et al. 2024a) (1970-2020). While certain patterns (pink) exhibit pre- dictable temporal regularities, abrupt changes (green) driven by external events necessitate the integration of textual in- formation to complement numerical forecasting.
2025d Wu al. 2025 (022) on this task. However, they solely rely numerical time series data, limiting their ability capture more complex patterns.
Taking the unemployment rate data (Liu et al. 2024a) example, Figure Iilluttrates that certain abrupt changes in the time series are difficutt to predict solely from historical numerical patterns. However, by incorporating textual information, the model may get erucial guidance that helps fore-

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Published as a conference paper at ICLR 2026
cast sudden shifts, such as the unemployment rate spikes triggered by the 2008 financial crisis and the COVID-19 pandemic in 2020. This demonstrates that text serves as a valuable complementary modality, enriching time series forecasting with event-driven guidance that is not easily inferred from numerical data alone. Moreover, textual information predominantly contributes to capturing event-related dynamics, but lacks descriptions of subtle fluctuations. However, this missing infor- mation can be supplemented through time series modeling, By jointly leveraging these complemen- lary strengths, the model can better integrate the forecasting abilities of the two modalities, thereby breaking the limitations of single modality.
To explore the potential of textual information and effectively apply in time series forecasting, there exist two main challenges. The first challenge is insufficient text utilization. Some approaches incorporate prompts based on endogenous text (Kowsher al. 2025), such as statistical summaries or dataset-specific descriptions. While this text offers useful context, it largely overlaps with in- formation already present the time series. Consequently, they are unable to effectively model external drivers of temporal dynamics. On the other hand, some methods (Luo et al. 2023 Zhang ct al. 2024) leverage LLMs to embed exogenous text (Liu et al. 2024a), which has richer textual sources, such news and policy documents. These methods primarily focus on the representation- level fusion and are difficult to explore the deep semantic information, leaving the potential value of textual information untapped. The second challenge is effectively aligning two modalities to leverage their complementary strengths. While text provides crucial guidance for sudden shifts and event-related dynamics, time series modeling captures the subtle fluctuations and numerical trends that text cannot describe. However, the considerable modality gap between these modalities prevents existing methods from achieving effective cross- mooss integration.
Based on these insights, we propose VoT, a novel multimodal time series forecasting method that leverages Event-driven Reasoning and Multi-level Aligmment to effectively unlock the value of tex- tual information. Our approach employs a dual-branch architecture to integrate both exogenous and endogenous text for comprehensive time series forecasting. Event-driven Reasoning is per- formed through our Event-Driven Prediction Branch, which employs a generative pipeline that in- cludes template generation, summarization, and reasoning. This pipeline enables LLMs to extract more forecasting-relate information from exogenous text. Additionally, we introduce Historical In-Context Learning (HIC), which provides error-informed guidance from historical samples for reasoning. Multi-level alignment is designed to fully integrate the predictive capabilities of time series and text. At the representation level, we introduce the numerical prediction branch with an Endogenous Text Alignment (ETA). The ETA first converts temporal statisties into textual descrip- tions and then uses decomposed pattern extraction and decomposed contrastive learning to achieve alignment between two modalities. At the prediction level, we introduce the Adaptive Frequency Fu- sion (AFF) that dynamically adjusts importance and integrates the frequency components of outputs from the event-driven prediction branch and the numerical prediction branch. Based on dynamic frequency fusion, the AFF enables domain-driven optimization to achieve complementary advan- tages and address the varying dependencies on textual and numerical information across different datasets. Specifically, we make the following contributions:
We introduce an event-driven reasoning method to extract the forecasting-related informa- tion from exogenous text and obtain numerical predictions. It is enhanced by Historical In-Context Learning (HIC), which retrieves historical reasoning examples as prompts to provide error-informed guidance for reasoning. The method improves the reasoning ability of LLMS and unlocks the value of text.
We propose a multi-level alignment approach. Specifically, we introduce the Endogenous Text Alignment (ETA) for representation-leve aligmment and the Adaptive Frequency Fu- sion (AFF) for prediction-level alignment. Through comprehensive alignment, we achieve complementary advantages across both modalities.
We conduct extensive experiments on 10 real-world datasets from different domains and achieve state-of-the-art prediction accuracy. Moreover, we conduct thorough ablation and analysis experiments to demonstrate our effective utilization of text.
2 RELATED WORK
Time Series Forecasting, Time series forecasting has evolved from traditional statistical methods
(Hyndman & Khandakar 2008 Li et al. 2022) to deep learning approaches (Qiu et al. 20256).
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Table 1: Comparison of multimodal time series forecasting methods with text incorporation
Sub-entegorios
2025
T.mcLLM 20124b
2314
N1256
CMIN
2023
Maformer
2024
Dual 2024
Time-MMID 20224
2026
ENSPID [024]
GPT4MIS
2014
2025
CIK
Usage
Feature Extraction
Ressoning
Endogenons
V
Text Type
Exogenous
These methods be broadly categorized into MLP--bsed (Zeng et al. 2023 Yue et al. RNN-based (Chung et al. [2014] Wen et al. 2017 Cirstea et al. 2019), CNN-based (Sen et al. 2019
2025
Liu et al. 2022; Wang al. 2023). and GNN-based (Jin et al. 2024a Cheng et al. 2024 mod-
els. Among them. Transformer-based models have become particularly prominent, vvolving from
foundational attention designs (Vawwani et 2017 Zhou et al. 2021 to decomposition-ennaaie enhancedd
architectures (Wu et al., [021] Zhou et al. (222) and patch-based tokenization (Cirstea et al.) [0022] Nie et al. 2023). Beyond architecture design, recent work has advanced training paradigms through frequency-domain learning and distribution alignment (Wang et al. 2025b c). and broadened appli- cability to challenging settings such as probabilistic forecasting (Wu et al. 2005 non non-stationary and irregular series (Liu et al.) (2026) and exogenous variable integration (Qiu ct al. 2025e). Automated
architecture search Wu et al. 20224, foundation models (Shi et al., 2024;] |224 Liu et al. [202dd c 20256
Wang et al. 2024 Li et al. 2025), and ceneral-purposs. frameworks (Qiu et al. 2025e Wang al.
| Zhang et al. 2026) further aim to provide unified solutions across diverse scenarios. These models primarily focus the time series modality.
Multimodal Time Series Forecasting with Text Incorporation. Leveraging L.LSs for time series has attracted growing interest. Methods such as GPT4TS (Chang et al. 2025). TimeLLM (Jin al., 2024b), TEST (Sun et al.) 2024). LLM-Mixer (Kowsher et al. (2025), CC-Time (Chen et al. 2025aa) and CALF [Liu al. 2025a) focus on enabling LLMs to understand time series through various en- coding, alignment, and cross- cross-modal modal fusion approaches. AimTS (Chen et al., 2025b) and Time VLM (Zhong et al., (225)) further extend such efforts visual modalities. However, these approaches pri- marily rely on temporal information without extensive exogenous text utilization. In domains rich in multimodal data (e.g., finance, health, and geoscience). CMIN (Luo al., 2023). Modality-aware Transformer (Emami--oohal
et al. 2024), DualTimm (Zhang et al. 2024). and multi-modal associ- ation methods (Qiu et al. 2025a) have started integrating exogenous texts with time series, while MM-TSFLib (Liu al.] (024a) extends such integration across multiple domains. Building this, TaTS (Li et al. 2026) reveals that time-aligned text and series exhibit similar periodicity. FNSPID (Don et al. (024) and GPT4MTS (Jia et al. 2024) place greater emphasis on text processing by filtering and summarizing text to extract salient signals, while CiK (Williams et al. (025)) proposes metrics for evaluating textual utility. More recently, Chat'ime (Wang et al. 2025a) and Aurora (Wu et al. 2026) explore unified generative multimodal forecasting frameworks. As summarized Table I existing methods either use LLMs only for feature extraction or handle only one text type (endogenous or exogenous). In contrast, our VoT jointly leverages LLMs for both feature ex- traction and reasoning while supporting both text types, enabling more comprehensive multimodal forecasting.
METHODOLOGY
3.1 PROBLEM FORMULATION
Consider a time series X = (x1, X2, Xr.) E RLXN,, where L denotes the length of the look-back window and N represents the number of variables. Each time point x, E R is associated with textual information T,, As mentioned in the introduction, we divide textual information T, into two categories based on their source and characteristics.
Exogenous text Tex (Liu et al. 2024a) originates from external sources such as news articles, policy announcements, social media posts, describing events, contexts, and factors that influence the time series beyond the system itself. Endogenous text Ten (Kowsher et al. 2025) consists of structured textual descriptions derived directly from time series statistics.
The objective of multimodal time series forecasting is to predict the future H time steps Y (X1+1, X1+2 XL+H) E HxN by leveraging both historical observations and their associated
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Dataset Discription
Historical In-Context
Prediction Prediction
Dataset Template
Learning
Righ
mwm
Exogenous Text
Summary
template generating.
summaricing
middle
reasoning
Event-Driven Prediction Branch
lowe
Time Series
TS Encoder
Endogenous Text Alignment
Predict
middle
Endogenous Men Mon Encoder
Bowt
Adaptive Froquency
Numerical Prediction Branch
Figure 2: The architecture of VoT. The event-driven branch processes exogenous text through three-step generative pipeline with the Historical In-Context Learning (HIC). The numerical branch aligns endogenous text with time series via the Endogenous Text Alignment (ETA). The Adaptive Frequency Fusion (AFF) combines both predictions across frequency bands with adaptive weights.
textual information:
== T
(1)
where Y R represents the predicted values, FF denotes the multimodal forecasting model, and 0 encompasses all learnable parameters.
3.2 OVERVIEW
To maximize text utilization and enable mutual complementarity between textual and temporal infor- mation, we propose dual-branch architecture that comprehensively leverages both exogenous and endogenous textual information. As illustrated in Figure 2] our method consists of two complemen- tary branches that supports Event-driven Reasoning and Multi-level Alignment. The event-driven prediction branch primarily focuses on extracting predictive information from the exogenous text and the numerical prediction branch aligns time series with the generated endogenous text. For Event-driven Reasoning, the event-driven prediction branch extracts contextual information from exogenous text through three-step generative pipeline. The pipeline is powered by the Historical In-Context Learning (HIC). Multi-level Alignment is designed to fully integrate the predictive capa- bilities two modalities. At the representation level, the numerical prediction branch employs an Endogenous Text Alignment (ETA) to extract textual features aligned with time series patterns. At the prediction level, we introduce Adaptive Frequency Fusion (AFF). It fuses the frequency compo- nents of event-driven prediction and numerical prediction. Rather than assuming fixed influence of exogenous text on time series, AFF learns optimal fusion strategies directly from the dataset through Fourier decomposition and learnable frequency-spece weights.
3.3 EVENT-DRIVEN REASONING
To strengthen the reasoning capabilities of LLMs for time series forecasting, we propose three-step generative pipeline including tem- plate generation, summarization, and reason- ing. It converts exogenous text into event- driven numerical predictions enriched with contextual information. Additionally. we pro- pose the Historical In-Context Learning (HIC). keeps historical reasoning samples during training and retrieves similar examples as error- informed guidance during inference, as shown in Figure 3 The retrieved examples are cor- rected to provide guidance that helps avoid similar errors. By integrating these examples, LLMs are enhanced to generate accurate and error-informed predictions.
MMM E
Reasoning
Summary
Prediction
Correct
Training Inference
embedding
cD
Correction
Knowledge Base
Retrieval
Summary
Reasoning
Prediction
Figure 3: The processing procedure the Histor-
ical In-Context Learning.
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3.3.1 GENERATIVE PIPELINE
To process exogenous text T into numerical predictions, we implement three-step generative pipeline. First, LLM generates a dataset-specific template 'D based on the dataset description and exogenous text-time series samples, which serves as structured dictionary containing key-value mappings that guide the extraction of predictive information. Second, we use this template 'D to generate summaries S; from raw exogenous text T and time series X,, filtering redundant infor- mation while preserving the information influential to time series forecasting. Finally, we employ the Reasoner, an L.M in the form of reasoning model, specialized for logical deduction and nu- merical forecasting rather than general natural language generation. It process these summaries and generate predictions:
(2) The reasoning model Reasoner generates both the numerical prediction Y E "NN and the explanatory reasoning process R, Preason is carefully designed prompt that guides the LLM to perform structured reasoning from summaries S; and time series X; to numerical predictions. An example in Appendix E showing Premon. S; and R2 can be quite helpful for understanding the roles of these items. However, this basic pipeline operates in unsupervised manner, which may introduce suboptimal guidance for numerical prediction patterns, potentially amplifying prediction errors. Accordingly, we design the Historical In-Context Learning (HIC) to solve the problem.
Yveenn R2 = Reasoner (Preason S, X;)
3.3.2 HISTORICAL IN-CONTEXT LEARNING
To make the prediction more accurate, introduce Historical In-Context Learning (HIC), which synergizes historical reasoning information with In-Context Learning (ICL), as shown in Figure During training, it corrects the reasoning process with ground-truth and keeps the corrected samples. The optimized reasoning process of the corrected samples keeps information about why errors exist and correct reasoning strategies. During inference, it retrieves the most similar historical example and integrates it into the reasoning prompt to obtain error-informed guidance and accurate forecast.
Knowledge Base Construction. During training, HIC builds a Knowledge Base by learning from prediction errors. As mentioned, the Reasoner generates initial predictions Yreven and reasoning processes R, After that, the Reasoner creates corrected reasoning C, using ground truth Y;:
C = ReeaoonerPooncee Poonect: Yevem R,, Y,,
where Peoreec is the prompt that guides the Reasoner to identify and understand what caused the error between the initial predictions Yeevent and the ground-truth Y,. Correction C, explains how to accurately derive the actual values given the context, and crucially, it contains analysis of the previous prediction errors, which helps Reasoner better understand and avoid similar errors in the future. The Knowledge Base stores pairs of summary embeddings of summaries {S,) ) and their corresponding correction {C: LM
K ((Embed(S.). C.)
where M denotes the number of training samples, Embed is an embedding model.
Retrieval-Guided Prediction. During inference, HIC retrieves the most similar historical example from K. to obtain euror-infrmmed guidance for forecasting. Given a data pair (X,, T, first LLM generates the summary Sy. Then using the embedding of the summary Sz. HIC retricves the most similar example in the Knowledge Base:
arg max simi(Embed(())) Embed(S,)) ,mb (S,C,)EK
where simi is the similarity metric. The retrieved corresponding correction C; serves as an in-context example, improving the reasoning accuracy with infor-ef guidance:
Seven Reasoner(Pica.C..SS,
(6)
where Picr. is the prompt that combines the retrieved correction C; as guidance for current inference.
By retrieving historically corrected reasoning patterns, HIC provides error-informed guidance for L.Mss. The emrorinformedd learning helps the model understand how exogenous text impacts time
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series, thereby improving predictions when similar event-driven fluctuations occur. Moreover, HIC achieves this without requiring expensive fine-tuning, making it efficient and scalable across differ- ent forecasting domains.
Event-driven Reasoning generates numerical predictions by leveraging semantic information in ex- ogenous text and capturing event-driven dynamics. Through correcting the reasoning process, con- structing a knowledge base, and implementing a serriev--nndguiie mechanism, our approach en- hances the reasoning ability of LLM while maximizing text utilization.
3.4 MULTI-LEVEL ALIGNMENT
While the event-driven prediction branch excels at capturing external influences through exogenous text, not all temporal variations are reflected in captured by exogenous text. Some data follow intrinsic patterns that are better captured through numerical analysis. To fully leverage the advan- tages both modalities, we design a Multi-level Alignment method. performs representation- level alignment with endogenous text via the Endogenous Text Alignment (ETA) of the numerical branch. In the prediction level, employs Adaptive Frequency Fusion (AFF) to align exogenous text prediction with numerical prediction and integrate results from both branches The outputs obtained after deep alignment obtain the complementary advantages of both modalities.
3.4.1 REPRESENTATION-LLVVEL ALIGNMENT
To achieve representation-levd alignment, the numerical prediction branch employs En- dogenous Text Alignment (ETA), as shown in Figure This module establishes deep seman- tic alignment between temporal patterns and their textual representations using decomposed pattern extraction and decomposed contrastive learning. Considering that trend and seasonal- ity are intrinsic properties of time series, ETA similarly extracts corresponding textual repre- sentations to achieve fine tee-grained alignment be- tween endogenous text and temporal patterns.
K&V
K&V
Dual-Query Attention
TS-Text Attention
K&V
Proj
Projas
Pesompseed coutrsstt bociing
BOB
TS Testaaass
Contrastive Loss C
slion
Embedding
SSN
Tuslaand
Figure 4: The processing procedure of the En- dogenous Text Alignment (ETA).
Time Series and Text Encoding. We encode the input time series E TR. L*N using an en- coder to obtain temporal representations H E R Nxd" Simultancously,
generate endogenous text Ten by converting statistical descriptors such as mean and frequency into structured textual descriptions, which are then encoded using an LLM to obtain text embeddings Hext E R xdd where L and [ denote the sequence length and text token length respectively, is the number of variables, and d=s ddesi are the embedding dimensions for time series and text respectively.
Decomposed Pattern Extraction. Our decomposed pattern extraction first employs the dual-query attention filter semantic components from text. First, we use learnable queries Q", Q N to extract trend and seasonal information E representations:
E
Nxd" related to time series from textual
E Attention H text Hiet),,
E Attention Hext H
Then, the ETA performs ts-text attention to align temporal representations with extracted textual components, obtaining further aligned representations " and 7. (cross- modal aligned features):
Z Cross Cross-AAtention/ (Pro E", E"),), E (tr,
where Proj(-) : Td d projects time series embeddings to the text embedding space, and Z - E "N are the aligned trend and seasonal representations that combine information from both modalities. To map these representations to the time series space for fusion, apply:
7 Proj (Z"), (tr, se}

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where Projinv (:) Rd + FR yielding 7 e ER
Deep Semantic Alignment via Decomposed Contrastive Learning. To achieve deep semantic alignment between temporal patterns and textual representations, employ contrastive learning at the sample level. We first decompose the temporal representations H. into trend and seasonal components to obtain H. and Hę. Then, we get the mean representation H. H E Rd and ZV 2 E Rdd
We compute the contrastive loss for each component pair.
1 Σ log 2
exp(sim H 2,)) Σ j==1 (xp(sim((H)))))cc H 2,))
exp(sim( Zi H))) log Σ/ exp(simm Z H)))
Calign
* * {tr, se}
(10)
where sim(:") denotes cosine similarity and B is the batch size. The total alignment loss combines both trend and seasonal components. This objective ensures that corresponding trend and seasonal components from both modalities aligned in shared representation space.
After completing the modal alignment, the numerical prediction output Y num is generated by fus- ing temporal and text representations. Specifically, the calculation formula of Y num is defined as Y num 2 H / (7 ) where H represents the original temporal representation, and (Z", 7 denotes the text text-derived representations corresponding to trend and seasonal components respectively. The fusion process adopts equal weight allocation to balance the contributions of tem- poral and textual information.
3.4.2 PREDICTION-LEVEL ALIGNMENT
Event-driven predictions excel at capturing patterns influenced by external factors, while not all tem- poral variations are reflected in captured by textual information. These complementary strengths can potentially integrated through frequency-based approaches. Therefore, at the prediction level, we introduce Adaptive Frequency Fusion (AFF) dynamically adjust the importance of different frequency components, thereby leveraging the complementary strengths textual and numerical information across frequency bands.
Adaptive Frequency Fusion. We decompose both branch predictions into frequency components:
Frum FFF(
Fevent FFT (Yeen))
(11)
The spectrum is partitioned into three bands based on frequency. For the detailed adaptive parti- tioning strategy and full implementation details, refer the reader to Appendix A.4 We extract band-specific components with mask M:
F =ー F. M * (num, event). e {low, mid, high}
(12)
Instead of using fixed fusion ratios, we introduce learnable weights w wh, [num, event), (low, mid, high} that adapt data characteristics:
Frused ΣΣ W.Ftt evem). (low, mid, high), final iFFT(Fiseed
(13)
Training Objective. Our model is trained with a composite loss function:
Liotal Las + Laligo + Linal
(14)
where Lss = MSE(Ý, &Y, ts. Y) maintains base temporal prediction capability and Y is from the nu- merical branch without ETA. Calign enforces cross-modal alignment via contrastive learning, and Linal = MSE( V fimal, Y) optimizes the fused prediction accuracy.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. We evaluate VoT on 10 worl-world multimodal time series datasets. 9 datasets sourced from the MM-LSF benchmark (Liu et al. 2024a), covering diverse domains including Agricul- ture, Climate, Economy, Energy, Public Health (United States), Environment, Traffic, and Security.

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Table 2: Forecasting results of time series-only and lext-enhanced methods, and our method VoT. The best results are highlighted in bold, and the second-best results are underlined.
Calegoey
Time scries-ouly
Texi-uehanced TErnnfoonmer
PanchTST
21223
(Transformer (10244
RaFT
PatchTST
Metrie
MSE
MAE
MSE
MAE
MSE
MAG
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
Agriculuue
0.209
0.302
0.225
0.301
0.220
0.308
0.226
0.322
0.232
0.316
0.225
0.310
0.216
0.335
0.840
0.888
0.926
1.178
0.858
0.944
0.201
103553
0.210
0.363
0.223
0.378
0.265
0.411
0.219
0.370
0.213
0.367
0.275
0.420
Energy
0.222
013443
0,250
0.363
0.269
0.382
0.254
0.367
0.253
0.365
0.265
0.383
0.246
0.366
Envirnmmnt
0.268
11,380
0.317
0.395
0.276
0.386
0.339
0.423
0.318
0.397
0.278
0.390
0.357
0.422
Health
1.205
0.714
1.432
0.504
1,519
0.833
1,833
0.975
1360
0,768
1.313
0.915
1,788
0.963
Security
70.111
3.937
72.027
4.062
75.042
77.204
4.473
72.721
74.032
4.154
76.587
Social Good
0.504
0..89
0.944
0.475
0.961
0.463
0.968
0.484
0.909
0.427
1.027
0.515
0.970
0.477
Traffic
0.169
01.332
0.176
0.234
0.184
0.238
0.258
0,382
0.174
0.239
0.184
0.237
0.300
0.394
Weatber 0.968
0.706
1.145
0.751
1.231
0.803
1,099
0.746
1,066
0,707
1.001
0.709
1,096
0,745
counts
Table 3: Forecasting results of multimodal methods and VoT. The best results are highlighted in bold, and the second-best results are underlined.
Models
VoT
GPT4TS
2025
GPT4MTS
Time VLM
CALF
2025a
Metric
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAB
Agriculture
0.209
0.302
0.220
0.294
0.225
0.298
0.215
0.301
0.238
0.303
0.250
0.315
Climate
1,078
0.840
1.184
0.891
1.182
0.889
1.180
0.887
1.195
0.899
1.286
0.922
Economy
0.201
0.353
0.217
0.371
0.208
0.363
0.215
0.368
0.229
0.384
0.207
0.357
Energy
0.222
0.244
Environment
0.268
0.380
0.322
0.393
0.323
0.400
0.319
0.396
0.320
0.398
0.325
0. 3.787
Health
1.205
0.714
1.341
0.777
1.464
0.799
1,356
0.767
1.490
0.835
1,491
0.775
Security
70.117
3,937
71.165
4,047
71.487
4.068
72.406
4.097
73.731
4.182
76.376
4,300
Social Good
0.804
0.389
0.917
0.476
0.920
0.450
0.918
0.428
0.869
0.444
0.906
0.401
Traffic
0.169
0,232
0.206
0,266
0.203
0.261
0.179
0.238
0.217
0.320
0.222
0.293
Weather 0.968
0.706
1.048
0.708
0.986
0.711
1.037
0.706
1.061
0.717
1.098
0.714
counts
Notably, the original Economy dataset in MM-TSFLib is arranged in reverse temporal order. We reorder this dataset temporally to align with the natural progression of events and maintain temporal consistency. Additionally, we introduce new Weather dataset containing multimodal meteorolog- ical observations. We follow the experimental settings from MM-TSFlib (Liu et al. 2024a) for dataset preprocessing and forecasting configurations. Detailed dataset descriptions provided in the Appendix A.I] Notably, we do not apply the "Drop Last" trick to ensure a fair comparison following the settings (Qiu et al. 2024).
Baselines. We compare VoT against eleven representative baselines: three time series-only meth- ods including iTransformer (Liu et al., 2024b), PatchTST (Nie et al. (202)). and RAFT (Han et al. 2025); three text-enhanced variants l'Transforme** PatchTST* and RAFT that incorporate textual information through the Time-MMD framework (Liu et al., (022a);; and five multimodal forecast- ing methods including GPT4TS (Chang et al. (025). GPT4MTS (Jia et al.) 2024). TaTS (Li et al.) 2026). Time-VLM (Zhong et al. (2025) and CALF (Liu et al.) 2025a).. All baselines are imple- mented using their official code repositories when available, with consistent experimental settings and hyperparameter tuning on the validation set.
Metrics. We adopt two standard metrics in time series forecasting: Mean Squared Error (MSE) and Mean Absolute Error (MAE). MSE emphasizes larger errors and more sensitive to outliers, while MAE provides a more interpretable measure of average prediction error. We report averaged results across all prediction horizons for comprehensive evaluation in section 4.2 with detailed results for individual horizons provided in Appendix G
We conduct a comparison of different reasoning-focusee LLMs (Reasoner variants) Appendix and all of them show consistent effectiveness in our forecasting framework.
4.2 MAIN RESULTS
Tables 2 and 3 present the outcomes of our comprehensive evaluation across 10 real-world multi- modal time series datasets. Our method VoT achieves the best performance on nearly all datasets, ranking first in all 20 metrics against time series-only and text-enhanced baselines, and 19 out of 20 metrics against multimodal methods. This dominant performance across diverse domains demon- strates the effectiveness of Event-driven Reasoning and Multi-level Alignment approach in lever-
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Exeri
RBC
Random
AFE
Nan
ETA
wio
VoT
only
ETA
ITC
Event
Statisties
0.379
04475
MSE
0.250
0.241
0.238
0.243
0.222
0.272
0.999
Energy
MAF
0.363
0,355
0.363
0.360
0.343
Social Good
MSE
0.944
0.840
0.845
0.876
0.804
Exogenous
MAE
0.475
0,424
0.410
0.436
0.389
Energy
Good
Table 4: Ablation study results on Energy and Social Good datasets
Figure 5: Ablation study different text sources for event-driven reasoning.
aging both exogenous and endogenous textual information. Notably, our method demonstrates su- perior performance across diverse domains, particularly excelling on datasets that are susceptible to event influence and rich in exogenous textual information, such as Energy and Social Good.
4.3
MODEL ANALYSIS
4.3.1 ABLATION STUDIES
To validate the effectiveness of each component in our model, we conduct ablation studies on the En- engy and Social Good datasets. Table presents the ablation results. The TS-only baseline achieves 0.250/0.944 MSE on Energy/Social Good, demonstrating significant performance gaps compared to our full method (0.222/0.804) Removing ETA (w/o ETA) degrades performance to 0.241/0.840 MSE, confirming that structured textual descriptions enhance pattern recognition. Without HIC (w/o HIC), performance drops to 0.238/0.84, MSE, with more significant degradation on Social Good, validating that the Strreee-andd-guie it, the LLM cannot be effectively guided in its reasoning process. Notably, removing HIC results in worse performance than removing the entire event-driven branch (w/o Event), suggesting that unguided [.LM reasoning can be more detrimental than no reasoning at all. When equipped with proper guidance, the event-driven branch becomes highly effective, as evidenced by the significant performance gap between our full model (0.222/0.800) 804) and the w/o Event baseline (0.232/0.841),
mechanism is crucial for event-driven forecasting. Without
4.3.2 EXOGENOUS TEXT IMPACT ANALYSIS
To verify whether the event-driven branch of VoT truly captures semantic information from text to enhance time series forecasting, we conduct a text replacement experiment. Specifically, we compare our approach using exogenous text with two alternatives: randomly generated text and statistics-based text derived from the time series, where the latter refers the endogenous text in this paper. As shown in Figure 5 results clearly demonstrate the superiority of exogenous text across both datasets. On the Energy dataset, exogenous text achieves 27.5% lower MSE and 14.3% lower MAE compared to random text. Similar improvements are observed on the Social Good dataset with 12.8% MSE reduction and 16.0% MAE reduction. The statistics show intermediate performance, suggesting that while time series-derived features provide some value, they cannot match the rich contextual information from real-world exogenous sources. This confirms that is of great significance for us to propose an wvent-driven prediction branch and incorporate exogenous texts into the multimodal time series forecasting methods.
4.3.3 ENDOGENOUS TEXT ALIGNMENT ANALYSIS
The reason for adopting ETA is that decomposition is simple and widely-used technique in time series analysis. By decomposing the time series into trend and seasonal components, we can enrich the semantic representation of the time series to certain degree. This enrichment allows for better alignment with textual information, facilitating more effective multi-modal interaction. To further validate the effectiveness of this approach, we conducted supplementary ablation experiments the numerical branch. The "w/o decomposition" condition indicates the absence of trend and seasonal decomposition, with only standard TS- contrastive learning being performed. The "w/o TS-Text

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CL" condition means that only time series decomposition is applied, without TS-Text contrastive learning. The experimental results, as presented in Table [ demonstrate that our proposed method, ETA, achieves superior performance compared to alternative designs.
Table 5: Ablation Study on Components of the Event-based Temporal Alignment (ETA)
Climate
Energy
SocialGood
Variant
MSE
MAF
MSE
MAE
MSR
MAE
w/o Decomposition
1.120
0.859
0.254
0.368
0.892
0.440
w/o TS-Text CL
1.184
0.888
0.250
0.363
0.944
0.475
ETA
1.092
0.848
0.232
0.350
0.841
0.410
4.3.4 ADAPTIVE FREQUENCY FUSION ANALYSIS
To further validate the necessity of the AFF, we conduct firquency-domain analysis on the Social Good dataset, as shown in Figure 6 We apply different frequency filters to analyze the frequency characteristics of each branch. (b)) the low-pass filtered signals (0-10% frequencies), the event-driven branch aligns more closely with the ground truth than the TS-only branch, demonstrat- ing its strength in capturing trend patterns and event-induced shifts; Figure (c), the high-pass filtered signals (70-100% frequencies), the TS-only method better matches the ground truth, effec- tively capturing short-term fluctuations and periodic patterns; In Figure (dg the band-pase pass filtered signals (10-70% frequencies) reveal increased similarity among all methods, indicating dissimilaritt much more prevalent in high and low frequencies. The choice of these frequency thresholds and the corresponding sensitivity analysis to the thresholds provided in Appendix These ob- servations empirically confirm that different prediction branches excel in distinct frequency bands, highlighting the need for frequency fusion methods. Since different datasets exhibit varying degrees of correlation with events, resulting in different frequency component distributions, adaptive fu- sion is necessary. This validates the necessity and effectiveness of our AFF over fixed combination strategies.
(a) Original
(b) Low-pass
(c) High-pass
(d) Band-pass
Figure 6: Frequency domain analysis of time series predictions (Social Good). (a) Original signals
without frequency decomposition. (b)-(d) Frequency-filters components: (b) Low-pass filtered signals, (c) High-pass filtered signals, and (d) Band-pass filtered signals. Ground Truth (blue), Time series-only prediction (pink), and event-driven prediction (green)
5 CONCLUSION
In this paper, we presented VoT, multimodal time series forecasting method that unlocks the value of textual information through Event-driven Reasoning and Multi-level Alignment. Our ap- proach leverages both exogenous and endogenous text via dual-branch architecture, where His- torical In-Context Learning enables LLMs to learn from historical prediction errors, Endogenous Text Alignment bridges the semantic gap between modalities. and Adaptive Frequency Fusion dy- namically combines their complementary strengths. Extensive experiments across 10 real-world datasets demonstrate that VoT achieves state-of- vfte--of-the-an performance, particularly excelling on event- influenced domains. Our work establishes that textual information provides irreplaceable contextual guidance for time series forecasting, and that effectively integrating these textual contexts with nu- merical patterns is crucial for advancing beyond the limitations of single-modality approaches.
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ACKNOWLEDGEMENTS
This work was partially supported by the National Natural Science Foundation of China (62406112, 62372179). Yang Shu the corresponding author of the work.
ETHICS STATEMENT
Our work exclusively uses publicly available benchmark datasets that contain no personally identifi- able information. The proposed anomaly detection framework is designed for beneficial applications in system reliability and safety monitoring. No human subjects involved in this research.
REPRODUCIBILITY STATEMENT
The performance of VoT and datasets used in work real, and all experimen- tal results can be reproduced. We have released our model code and checkpoints at http://ssstbbbooommmeeesssiiiiin
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A EXPERIMENTAL DETAILS
A.1 DATASETS
Our experiments utilize 10 multimodal time series datasets from two sources: MM-TSFLib Datasets (Liu et al. 2024a): We adopt 9 datasets from the MM-TSFLib benchmark, maintaining their original preprocessing and alignment procedures. These datasets span:
Agriculture: USDA broiler market prices with weekly market reports
Climate: NOAA drought indices with monthly climate reports
Economy: US international trade balance (chronologically reordered for our experiments)
Energy: Weekly US gasoline prices from EIA
Health: CDC influenza- like illness statistics
Environment: New York EPA air quality measurements
Traffic: FHWA vehicle miles traveled statistics
Security: FEMA disaster declarations
We introduce new multimodal dataset containing hourly observations of weather. Each data point includes four numeric variables (MINTEMP, MAXTEMP, HUMIDITY, MAXHUMIDITY aligned with natural language weather descriptions. The OT variable rep- resents the maximum humidity in local regions.
Weather Dataset:
We follow the experimental settings from MM-TSFLib (Liu et al. 2024a), with configurations vary- ing based on data reporting frequency:
For Environment and Weather datasets, we use a lookback window of L = 96 time steps, with forecasting horizons H {48, 96, 192, 336} and a label window size of 48 for the decoder.
For Health and Energy datasets, the lookback window is set to L = 36, with forecasting horizons H {12,24, 36, 48} and label window size of 18.
For Agriculture, Climate, Economy, Security, Social Good, and Traffic datasets, we employ a look- back window of L forecasting horizons H {6, 8, 10, 12), and label window size of
A.2 DATASET SPLIT AND TIME LEAKAGE MITIGATION
Our dataset split is time-based. The first 70% of the data in temporal order serves as the training set, the 70%-80% segment serves as the validation set, and the latest 20% serves as the test set. There is no temporal overlap between the training, validation, and test sets. When constructing the knowledge base, we exclusively use relevant data from the training set. During the inference phase, that is, when the input is from the validation or test set, retrieving examples from the knowledge database only returns training-ser set data. All these data are temporally prior the data in the validation and test sets, so no time leakage occurs.
A.3 BASELINES
We evaluate our approach against nine representative baselines spanning from pure time series meth- ods to multimodal forecasting approaches. The basclines selected based on their tiiee--ffthheeee performance and represent diverse architectural designs from recent years. The specific code repos- itories for each model are shown in Table
For the text-enhanced variants (marked with *), we integrate textual information following the Time- MMD framework implementation, which concatenates text embeddings with time series represen- tations before the prediction layer. All models are evaluated under identical experimental conditions with consistent data preprocessing and hyperparameter tuning protocols.
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Table 6: Implementation details and code repositories for baseline methods
Calegory
Method
Repository
Time Series
Transformer
https:://ntthhsb..om//huuut/ com Trars Former
https: /ttpss////ttttbt/t
Only
PatchTST
nttps://q1Entb http::///.aaaaa..ccooo/////aaa/s
RAFT
nttpa://gathub. hwt.:/aa....oaa.aacacccaaaaaaaaa http:///aaaa..oaaaaaaaaaaaaaaaaa
Transformer"
Based Transformer with Time-MMD integration
Text-Enhanced
PatchTST*
Based on PatchTST with Time-MMD text integration
(Time-MMD)
RAFT
Based on RAFT with Time-MMD text integration
Time-MMD
htLpa://g1thue com htw.e//allylllla.a. www.d/llaaaaaaaaaa..a
GPT4TS
htt 789521/L1MC33
GPT4MTS
http:/// S- based-
Multimodal
Forecasting
Lanquage Mode
TaTS
https:/ /ttps:///ithbb.. com. 1EAEAAA---AL--11 010C/1a01
TimeVLM
LLps: http://atuub.cccccccccyi.. hatp:////CCiiaaaaaaaaCCaCaa.C..a
https://gatnab htt.://aaa...oo//aanu///////////
A.4 DETAILED IMPLEMENTATION SETTINGS
Software Environment. Our implementation uses Py PyToh 2.5.0+cu121 with Python 3.10. All experiments are conducted Ubuntu 20.04 with CUDA 12.1.
Event-Driven Branch Configuration.
Template Generation and Summarization: We employ Ollama-wrapper Meta-Llamm-3-
8B-Instruct for generating dataset-specific templates and extracting event summaries from exogenous text.
Knowledge Base Construction: Historical patterns are embedded using Qwen2- Embedding-0.66 (768-dimensional vectors) for efficient similarity retrieval.
Reasoning and Optimization: During inference, utilize the DeepSeek API for gener- ating predictions and performing reasoning correction. The most similar historical patterns is retrieved based on cosine similarity.
Numerical Branch Configuration.
Backbone Architecture: PatchTST with 2-layer encoder (e layers=2).
Endogenous Text Generation: GPT-2 converts structured textual descriptions into repre- sentation.
Adaptive Frequency Fusion Settings.
Band Partition: Initial partition uses Low (0-10%), Mid (10-70%), High (70-100%) fre- queney bands. The actual boundaries adaptively adjusted based the frequency spec- trum characteristics of each dataset to ensure effective separation between Low, Mid, and High components.
We conducted tests under different boundary conditions evaluate the sensitivity of the model and presented the results in Figure of the updated version. The figure displays heat map of MSE or MAE values with respect to different low- and high-frequency bound- aries. Through analysis of the results, we observe that with the change of low and high frequency bounds, the results do not change significantly when the boundary selection is reasonable, indicating low boundary parameter sensitivity.
Weight Initialization: All six fusion weights w = |0.5, 0.5, 0.5, 0.5, 0.5) for balanced initial contributions from both branches across all frequency bands.
Multi-Stage Training Protocol.
Stage (Pre-training): Train PatchTST backbone for 10 epochs with MSE loss, learning rate le-4, cosine annealing scheduler.
Stage 2 (Alignment): Train alignment module for 10 epochs with contrastive loss, learning rate 1e-3, encoder weights frozen.
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Energy MSE
Energy MAE
SocialGood MSE
SocialGood MAE
0.24
0:36
0.4 0.3 0.2 0.1 High Frequency Bound
0.4 0.3 0.2 0.5
High frequency found
0.41
0.24
0.41
0.3 0.3 High Frosuenes
0.3 0.2 High Troqueric
0.35
0.43
0.35
0.23
Heat Map of MSE/MAE Values Based on Low- Low-Frequency and High-Frequency Percentage Cutoffs. The X-axis represents the percentage cutoff's for the low-frequency range, varying from 10% to 40% of the low-freqh wow-freuueccy spectrum. The y-axis represents the percentage cutoffs for the high- frequency range, ranging from 10% to 40% of the high-frequency spectrum. The color intensity in the map indicates the magnitude of the Mean Squared Error (MSE) or Mean Absolute Error (MAE)
Figure 7:
Stage 3 (Joint Optimization): Fine-tune all components for 30 epochs, learning rate lected from (5e-4, le-5} based on validation performance.
Code Availability. Lecisionnineellggnnee//oo
The code is made
available at https://gitthn htm.ttcubocoomomm...cmmmmmmmm
A.5 EFFCIENCY ANALYSIS
As shown in the Table the total latency is about 5.9s, which is mainly due the Event-driven branch. LLM indeed brings additional cost. However, text analysis can bring abundant and valuable information which can help break the bottleneck of time series forecasting. That's why need to investigate multimodal and additional cost brought by LLM is inevitable. But in most domains where multimodal needed, the data is always sampled at a low frequency due to the fact that the collection of text needs time. And the time gap is enough for us to do the next value predic- tion. Therefore, second-level latency is acceptable. And when there is high demand for real-time performance, we can use only the numerical branch, which is much faster and also effective.
Table Inference Latency of Main Model Components
Branch / Module
Inference Time (s)
Event-driven Branch
5.9
Numerical Branch
0.002
AFF
0.0005
B HISTORICAL IN IN-CONTEXT LEARNING DESIGN DESCRIPTION
B. THE ABLATION OF HIC
We compare our HIC method with simpler retrieval-based approaches and presented the results in the Table 8 the Table "No Retrieval" represents the baseline case where retrieval mechanism is used. "Retrieve TS" refers to the time series retrieval method, where the retrieval score is calculated as the cosine similarity between time series and the recalled data is time series. "Retrieve Summary" is the summary-based retrieval method, comparing the summary embedding similarity and recalling the summary. "Full HIC" indicates our complete HIC method.
As the results show, for the Climate dataset, the time series retrieval method slightly outperforms the "No Retrieval" baseline, but the improvement marginal. On the SocialGood dataset, the time series retrieval method even leads performance degradation compared to the "No Retrieval" baseline. The Hummary-based retrieval method shows a more significant improvement over the time series retrieval method in terms of performance metrics. However, complete HIC method achieves the best performance, which indicates that the performance improvement is robust and primarily attributed the full HIC module.
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Table 8: Ablation Comparison of HIC with Simplified Retrieval Methods
Dataset
Method
MSE
MAE
No Retrieval
1.151
0.877
Retrieve TS Only
1.137
0.866
Climate
Retrieve Summary Only
1.091
0.846
Full HIC
1.078
0.840
No Retrieval
0.845
0.410
Retrieve TS Only
0.853
0.421
SocialGood
Retrieve Summary Only
0.822
0.395
Full HIC
0.804
0.389
B.2 ROBUSTNESS OF HIC WHEN RETRIEVING WITH LOW SIMILARITY
We conducted supplementary experiments to compare the results under different scenarios similar example selection. Specifically, retrieved the 10th most similar and 100th most similar exam- ples to simulate the scenario where there are no close historical examples. The results are shown in the following Table g
From these results, it can be observed that as the retrieval range of similar examples changes (from the 1st-most to the 100th-most similar example), and in comparison with the no-retrieval case (equiv- alent to disabling some functions of the HIC module), although the model performance fluctuates, it generally remains relatively stable. This further demonstrates the effectiveness and robustness of our design under different conditions.
Table Model Robustness Imperfect Retrieved Examples
Dataset
Retrieved Example by Similarity Rank
MSE
MAE
Ist-most Similar Example
0.222
0.343
10th-most Similar Example
0.229
0.348
Energy
100th-most Similar Example
0.233
0.348
No Retrieval
0.238
0.363
Ist-most Similar Example
0.804
0.389
10th-most Similar Example
0.807
0.408
SocialGood
100th-most Similar Example
0.833
0.434
No Retrieval
0.865
0.410
B.3 ROBUSTNESS TO NOISY/SPARSE TEXT DATA
We manually created relevant datasets. In our experiments, we introduced two types of modifica- tions to simulate different real-world scenarios, For the sparse scenarios, we applied 10% and 20% masking the text the datasets. This included both discrete point masking (mask the text T(2) and continuous point masking (mask (T Te. To simulate noisy scenarios, we added 10% and 20 noise to the text data. The experimental results, presented in the Table [0] show that while the performance does decrease slightly. the decline is not substantial. Moreover, our method still outperforms single-branct methods. These results demonstrate the robustness of our approach to noisy sparse exogenous text. However, in extreme cases where all data is noisy or there is valid text information available, the decline may be significant. In practice, severely degraded text data can be flagged via quality checks, mitigating negative impacts.
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Table 10: Robustness Test of the Model Against Noisy and Sparse Text Data
Dataset
Condition
MSE
MAE
Mask 10% (mask 10)
1.099
0.850
Mask 20% (mask20)
1.109
0.854
Climate
Noise 10% (noise10)
1.098
0.850
Noise 20% (noise20)
1.103
0.854
Our Method (Original)
1.078
0.840
Mask 10% (mask10)
0.822
0,448
Mask 20% (mask20)
0.854
0.451
SocialGood
Noise 10% (noise 10)
0.836
0,412
Noise 20% (noise20)
0.876
0.480
Our Method (Original)
0.804
0.389
C ENDOGENOUS TEXT ALIGNMENT DESIGN DESCRIPTION
C.I COMPARISON WITH PARAMETER MATCHED ALTERNATIVES
we use gated-residual, cross-attention and FiLM for representation alignment and compare with ETA. The results are in Table T1 From these results, is clear that methods like gated-residual and FiLM and cross-attention have higher MSE and MAE than ETA. Which means the methods struggle effectively align the highly distinct representation spaces of time series and text.
Table 11: Ablation Study on Temporal Alignment Methods
Dataset
Method
MSE
MAE
Gated-Residus
1.230
0,912
Cross-Attention
1.202
0.893
Climate
FiLM
1.211
0.897
ETA
1.092
0.848
Gated-Residual
0.248
0.360
Cross-Attention
0.254
0.369
Energy
FiLM
0.252
0.367
ETA
0.232
0.350
0.893
0.431
Cross-Attention
0.887
0.427
SocialGood
FiLM
0.894
0,431
ETA
0.841
0.410
D ADAPTIVE FREQUENCY FUSION DESIGN DESCRIPTION
D. WHY CHOOSE LEARNABLE WEIGHTS INSTEAD OF LINEAR METHODS
Non-linear methods always have better fit ability. But we use learnable parameter in stead of most non-linear method here for following reason. We need the Event-driven branch to forecast unseen patterns that cannot be learned from historical time series data and correct the numerical prediction. The unseen patterns always cause distribution shift. And we visualized the OT values of these datasets as shown the Figure We observe obvious distribution shifts between the training data and the test data. Therefore, non-linear methods may overfit the patterns learned from the training data and generalize poorly on the test data. In contrast, AFF uses only 6 parameters and aims to reflect the influence of text time series, which generalizes better. We compare the results and provide them in the table below. The results in Table [2] show our AFF performs better than the non-linear methods, which support analysis.
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Figure 8: The visualization of the datasets
Table 12: Performance Comparison of Fusion Methods on Multiple Datasets
Dataset
Fusion Method
MAE MSE
MLP
1.228
0.875
Climate
1.768
1.055
AFF
1.078
0.840
MLP
0.685
0.591
Energy
Cross-Attentio Attention
0.430
0.502
AFF
0.222
0.343
MLP
1.514
0.806
SocialGood
1.459
0.842
AFF
0.804
0.389
D.2 EVENT AND NON-EVER PART ANALYSIS
To distinguish the event part and the non-event part in the data, we visualized the test datasets of Energy and SocialGood dataset and visually identified event segments (sudden shifts) and non- event segments (stable periods) in the test sets. Subsequently, the predictions of the two periods from two branches (the event-driven branch and the numerical branch), well the fused final prediction, in comparison with the ground truth. The results presented in the Table 13
computed the MSE and MAE of
Upon analyzing these results, observed that during the event period, the event-driven branch performs better than numerical predictions, and during the non-event period, numerical prediction better. During the event period, the fusion method outperforms the numerical method, and during the non-event period, the fusion method is better than the event-driven method. This means the two predictions are complementary. For both the event part and the non-event part, the fusion method is better than both the event-driven and numerical prediction methods in some cases. For 'Overall', the fusion prediction is always better than the others, which is what we want. This can serve as proof that event-driven reasoning brings greater benefits to the event-related part than the harm to the non-event part, and ultimately leads to a better result.
We analyzed the Pearson coefficient between various frequency components and the ground truth to compare the correlation. The experiment is conducted on SocialGood dataset. The results are presented in the Table 14] As previously mentioned in the paper, event-driven predictions low-frequency components tend to be closer to the ground truth, whereas numerical predictions on high-frequency components closer the ground truth.
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Table 13: Prediction Performance Breakdown in Event and Non-Event Periods
Energy
SocialGood
Period
Branch
MSE
MAE
MSE
MAE
Event-driven Branch
0.402
0.450
3.080
0.979
Event Part
Numerical Branch
0.443
0.523
3.339
1.022
Fusion Result
0.362
0.460
3.084
0.969
Event-driven Branch
0.266
0.362
0.116
0.270
Non-Event Part
Numerical Branch
0.105
0.257
0.068
0.208
Fusion Result
0.125
0.269
0.066
0.205
Event-driven Branch
0.314
0.391
0.832
0,441
Overall
Numerical Branch
0.244
0.360
0.861
0.405
Fusion Result
0.222
0.343
0.804
0.389
Table 14: Pearson Correlation of Frequency Components (SocialGood)
Pred Length
Comparison
High Freq
Low Freq
Event-driven Ground Truth Numerical vs Ground Truth 0,423805
0,155412
0.497763
8
0.458464
Event-driven vs Ground Truth 0.265945
0.402431
Numerical vs Ground Truth 0.349408
0,381150
Event-driven vs Ground Truth 0.273838
0.466686
12
Numerical vs Ground Truth 0.310766
0.436835
E LLM GENERATION EXAMPLES FOR EVENT-DRIVEN PREDICTION
This section demonstrates the complete event-driven prediction pipeline through concrete examples from our US unemployment rate forecasting experiments.
Template Goneation
Rooening
Sumsary
Dcuription
Dabeet
Sumwory Trinpinn
Original Text
Suxurary
Zevept
Tratel Beaening
Facqg
Optiiizdd Reousing
Summury
Optinizod Reaesuzg
Plaege
end
Sarrery
Original Tixt 9eaz
Proeee
Optinical H611
Optinnect A611
4831
261l
#832
Poiex
Optiitoal
Optirised
Renoning
Rooweht
Infor
Original Text N768
#719
Eatodd
hrary
Reaezitu
K710
Figure 9: Illustration of the LLM generation pipeline for event-driven prediction. The process flows from template generation through summarization to reasoning, enhanced by Historical In-Context Learning that provides error-informed examples guidance for improved forecasting accuracy.
E.1 DATASET TEMPLATE GENERATION
Prompt Template:
prompt gensyou are prefessional scientist. Based on records (sample dasa paica) and dataset deseription/daaaase description), generate a summory template for value prediction domain knovledge...
Generated Template (SocialGood Dataset):
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"Dalaset Kame"; "Onemploymen: Rate CT Value Prediction Dataset",
"Description": "Konthly unemployment statistics for the United States, saggragatedd by race",
"hata Type*; "Time series data,
data",
"Data Source"; *0.8, Department of Laboz, BLS", Span";
present imonthly datal, to present Jannsal reports), 1979 to 1980 (text data)",
"Sempling Method"; "Cemprehensive dats from officisl reports", "or Value"; "Unemployment Raze", "Signi ficance*; "Indicator of econonic health and societal impact", "Possible telationships";
"Temporal": "OI value changes over time", loyment rates", "Causal"; "Beonomic policies demographic changes"
"Spatial"; *Racial disparicies
"Features for Prediction": {
"Trenda in Time Series": "Ristorical trends and sessonal bessonality",
"Key Information Text Content"; "Bconomic ind_catore and policy
changes",
"Demain knowledge"; "Economics,
markets, demographic trends"
"rend analysia for Prediction"; {
"Short-Term Trends"; *Recent fluctuations the volue", "Song-Term Trenda"; "fistorical patterns and future tonse",
"Cyclical Trends*; "Business cycle impact", "Demographic Trends"; "Changes in population size and structure"
E.2 SUMMARY GENERATION
Prompt Template:
base_promp = f="*"ou are a professional. data scientist analyzing a speci fia time window of TASK
Generate a NEW UNIQUE snelytical sunmary this specific window simert_tmme] to sin]_time] of the dataset_nas name dataset.
REFFRRNCE TNFORMATTON (D0 COPY-ONTY CONTEXT) (dataset descriptien]
REOUTRED OUTPCT STRUCTURE output
this JSON structure, with YOUR ORN analyeis:
{template)
2A1A ANALYSE
Time Series Data for this window: (stre_series_at) for this window: (texta_str) ***
instructicns
IMPORTANT INSTRUCTIONS:
1. Your octput must be valid JSON object only.
2. NOT COPY descrigtions from the reference information!
CREATE NEW enalysis specific THIS window
4. Focus on the trends, patterns, and insights from THIS apecitic time period. happening
analysis should reflect
provided series
cext data.
Respond
requasted JSCN object. ***
Training Window #611 Summary (Dec 2000-Jul 2001):
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"Dalaset Kame"; *00 Racial Unemployment OT Value Prediction ion DataseL", "Description": "Konthly unemployment statistics for the United States, daggraageeed by racial groups",
"hata Type*; "Time series data,
data", "Data Source"; *0.8, Bureau of Labor Statistica (BLS)",
to March "Sampling Method"; "Comprehensive data from official employment reporta", "cr Value"; "Average Unemployment Rate Across Al1 Groupe", "significance": "indicator of workforce dynamics and policy implications", "Possibla Rolationshigs":
Span": : "Auguat
(monthly data)",
"Temporal"; "Seascnal fluctuations influenced macroecoromic faczore", "Spatiel"; "Regionsl differences impacted by industry composilion and akt requi rementa",
"Causal"; "GDP changes, monetazy policy, and post 9/11 sconemic impacts"
"Features for Prediction":
"Trends in Time Series": "Aistorical seasons lity and unmmployment
palterns",
"Key Informetion in Text Content"; "Kacroeconanic indicstors and industry composition changes", "Domain Knowledge"; "Labor economics, recial demographics, and economic shocks"
"Trend Analysta for Prediction": {
"Short Term
"Overall downward tread during specified period (90s
confidence)".
"Long-Tern Trends"; "Ristorical unemployment patterns across
groups",
"Cyclical Trends": "Increased variability post-8/11 attacks (808
conft dence)
"Demographic Trends"; "Vaciations in unemployment zates by racial group"
"or": *44.9, 4.7, 5.0, 5.3, 5.4, 6.3, 6.2, 6.21*
Test Window #719 Summary (Dee 2009-Jul 2010):
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"Dalaset Kame"; "0S Unempleyment Tzend Analysie", "Description": "Analyzing the trend unemployment in the US from Dec 2309 to Jul 2010.",
Type": *Mixed time series and data",
"Data Source"; "Buresu of Labor Statistica (B18)", Span": *hac 2009-Jul 2010",
"Sampling Method"; "Regulár reporting from multiple sources including goverument agencies and scademic institutions";
"UT Value"; "US Unemployment Aate", "Signi ficance*; "Indicates the overoll health of the us economy and pocential Impact aociety",
"Poseible Relaticnships":
"Temporal"; "Seasonal "luccuations Matorica: trenda intluenciing carzent unemployment sale", "Spetial"; "Segionsl differences in unemployment retes indicating locslized economic conditions"
}
"Features Prediction"; {
"Irends in Time Series"; "Identifying short and mediun-test term treads past performance",
"Key Informstien Text Contens": "Understanding the underlying causes of unemployment through contextuel analysis",
"Domain Knowlodge"; "Familiarity with nacroeconomic factors affecting emp employment rates"
"Izend Analysis for Prediction":
"short-Torm Trends": "Notable dealine in unemplayment rate reaching values",
in Mar-Apr 2010 followed by slight rebound before stabilizing initial
"Sorg-Term Trenda": "Gradval upward
sep-Dee 2009 indi
stabilisation of workforce participation"
"Cyclical Trends*; "Influence of business cycles contributing periods accelerated hiring and layoffs throughout caservation window", "Demographic Trenda"; "Shift towaré part-time/fu 1-time employment. distributions possibly linked to seasonal varistions in service activities"
"oT": "19.7, 20.6, 10.4, 10.2, 9.3, 9.3, 3.6, 3.71"
E.3 INITIAL PREDICTION AND CORRECTED REASONING
Prompt Template:
prompt gen*<e>> [INS1] You quancitative anslyst specializing in multimodal
time series foresasting.
Nindow bength: (window_ate
Prediction Length: (predici itoeggaaion_eemgua
(dstaset__nmm) using textual Predict next (pradiction wetecetooonnooeee langenghh) sunmary and kindow data.
ANALYSIS:
Textual intelligense: Extract insights from surmery
2. Numerical Fattarns: Identify crends and cycles
Comain Knowledge: Apply sector understanding
EXAMPLE: [Eixed_example]
SUMMARY: (aunmary)
OUTPUT: JSON format exactly:
'13son
"Pradiction": "*, "Reasoning". *Brief explanation of key factora driving the prediction"
Initial Prediction for Window #611:
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"Sunmary...
"predictos__values"' [6.0, 5.3, 5.6, 5.5, 5.4, 5.3, 5.2, 5.1, 5.0, 4.9, 4.8, 4,7),
"actual Lyvauea": [3.7, 5,8, 5.0, 5.7, 5.4, 5.3, 5.7, 6.0, 6,4, 6,2],
foriginals _reasoning": "The prediesion reflecta gradual downword unemployment rates, consistent with the 908 confidence level for overall decline. The initial higher values account for post-9/11 9/21 variebility (508 confidencel, followed by stabilization seasonal adjustments and improving cconamic indicators suggest a return to 12-monta horizon.",
macroezonomic rezovery takes effect.
pre-shook levela "ot_values"; *[4.9, 4.7, 5.0, 5.3, 5.4, 6.3, 6.1, 6.1]"
Prompt Template:
prompt <e"*<o> (INST] You are expert analyst tasked with improving pradiction reaaoning by learning from actuall outcomes.
DACASET: daLazet_name
#INDOW SUMMARY: [sumnary]
PREDICTION ANALYSIS:
original otedictions (Original Predictiion]
Actual Values: actual_stz
ORTETNAT, RRASONTNG: periginal_reazoning]
TASK: Rewrite the reasoning process by inoosporatinng insights from the actual outcomes. Focus
1. Pattern Aecognitien: What patterns in the actual data were missed or correctly idantified?
2, Factor Analysts: Which factora had stronger/veaker impact than initially predicted?
3. Trend Understanding: Rew did the actual trend differ from expectations
4, Domain Insights: What donain specific knowledge can be extracted from this comperisen?
cUreUT: JSCN format exactly:
'gson
{
"Improved Reasoninne "Comprehensive anslysis incorporating
and Improved underatanding",
Leey_nnsghht"" "3-5 bullet points of key learnings from this prediction vs reality comparison",
oocciittrrooaar "Updated understanding of the most importans pradiction factors
[/INST)</<3")
Corrected Reasoning:
"Summary"
gini _reasoning..."
"improved Sepproved_eeaooniii reasoning" "The original prediction snticipated gradual downward trend in unemployment rates, influenced by post-9/11 variebility and complex pattern,
macroeconomic recovery. HeNevar, the actual data revesled with fluctuations that did not align with the expected stab lization, Notably, an upward
actual unemployment rates showed heighsened variability toward the end of the period, contrary to the predicted dealine. This discrepancy siggests that the initial model underest imated imatee the lingering effects of the 9/11 attacks and overestinated the speed of econonic insights, future predictiens should secount for prolenged external shocks and thair nonlinear inpacta mamployment trends.",
Incorperating these
upward end of the perlod, contrary to the predt cted gradual decl ine.",
"the actual data exhibited more variabilisy
"The lingering effects of the 9/11 attacks had more prolonged and severe impact unemployment
than initially anticipsted.",
for "Xacrosconomic recovery was slover than predicted, highlighting the need astimates poat-cris's scanarios.* "Seasonal adjustments and industzy composition changes may have been lees pentod."
influential than
"peediction "Updated understanding emphasizes the imporsance of: accounting for prelonged external shocks (e.g., gespolitical events), 2) adjusting recovery timelines hased en real-time economic indicators, and 31 tncorporating noni inear Impacts macrosconomia factors unemployment rates."
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E.4 HIC- MUC-GUIDED PREDICTION (TEST PHASE)
RAG Retrieval for Window #719:
"simi lar las_iilrr_xxaplleee example_windex". : E11,
"aimilarity score": 0.6218101086616511
"retrieval_methoow *Bunma Saeed___________eeeeeeeee_ RaG_Enbedd_ng
Prompt Template:
prompt <*"*<>> (TSST) You are a quancitativv analyyst specializing in multimodal time series forecaating,
Nindow Length: (vindow_size)
Prediction Length: (predicsion
TASK: Predict next gpadicttion_langth) values for (dasaact_name) using textual aummary and window data.
ANALYSIS:
1. Textual Intell gences: Extract insights from aunmary
Numerical Patlerns: Identify icenda and cycles
3. Domsin Knowledge: Apply sector understanding EXAMPLE: (retrievad_examplee
SUMMARY: (aumnary)
OUTPUT: JSCN format exactly:
'JJson
"Preciction": "",
"Ressoning": "Drief explanation key factors driving the prediction*
[/INST<</5>**
Generated Prediction with HIC:
{
"prediction"; [9.8, 9.9, 10.1, 10.2, 10.0,
9.7, 9.6, 9.5, 9.4, 9.3, 9.21,
"reasoning": "Analysis suggesta gradual stabilization following
pattern from simila historical. window. "ncorporates inaighta
about underestimating short-term fluctuations. Prediction reflects halanced vi considering cyclical learned from Kindow 4611's ercor patterns."
potential volatility
E.5 ANALYSIS OF EVENT-DRIVEN DESIGN
The above examples vividly illustrate how our event-driven prediction approach transforms the chal- lenge of multimodal time series forecasting into a continuous learning process. Consider the journey of Window #611 during the 2000-2001 economic downturn: while the initial prediction correctly identified an upward trend in unemployment, it projected smooth, gradual increase from 4.8% to 5.9%. Reality proved far more turbulent-une unnuuennt-unnmlymmeenn spiked sharply to 6.3% following the September 11 attacks before showing unexpected resilience in recovery. This discrepancy between prediction and reality became a valuable learning opportunity. Through error analysis, the system discovered that external shocks create discontinuities that pure trend analysis cannot capture, lead- ing to a fundamental recalibration of prediction weights: historical trends were reduced from 40% to 30% importance while economic shock factors increased from 20% to 35%. This learned knowledge proves its worth when the system encounters Window #719 during the 2009-2010 financial crisis period. Through embedding similarity analysis, the system recognizes a kindred pattern between these two economic downturns, achieving similarity score of 0.8278. This recognition triggers the retrieval of Window #611's hard-won insights about volatility and recovery patterns. Rather than repeating the mistake of over-smooth predictions, the forecast for Window #719 now incorpo- rates the understanding that crisis periods exhibit both sharp fluctuations and surprising stabilization mechanisms. The resulting prediction balances the observed stabilization trend with awareness of potential volatility, producing a more nuanced trajectory from 9.8% gradually declining to 9.2%. What makes this approach particularly compelling is how it mirrors human expert reasoning in eco- nomic forecasting. Just as economists draw parallels between historical crises to inform currett predictions, HIC systematically captures and transfers these insights across similar economic conditions. The system essentially builds a memory of how past events translated into numeri- cal outcomes, creating bridge between the rich semantic information in reports, policy
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nouncements, and economic analyses, and the quantitative requirements of time series forecasting. This design achieves what pure numerical models struggle with understanding that phrase like "unprecedented economic shock" carries specific implications for unemployment volatility based on historical precedent-whie avoiding the computational expense of fine-tuning large language models for each specific domain.
F ABLATION STUDY OF DIFFERENT REASONING MODELS
Table 15: Ablation study different LLMs
Energy
Social
Good
LLM
MSE
MAE
MSE
MAE
DeepSeek-RI
0.222
0.343
0.804
0.389
GLM-4.5
0.229
0,337
0.829
0.411
Qwen3-8B
0.226
0.341
0.773
0.427
We also attempted to conduct experiments using different reasoning models. The experimental results showed in Table [15] indicated that under our method, LLMs could all provide excellent assistance for time series forecasting and outperform other methods.
G COMPREHENSIVE EXPERIMENTAL RESULTS
This appendix provides comprehensive experimental results for all methods across different predic- tion horizons on 10 multimodal time series datasets.
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Rable 16: Model anoommance Comparison Acroos
Calegories cifferrent
MAB
0.250
0.520
0,355
0,315
0,910
09905
08980
0.883
0,922
0.334
0.37
00353
0.363
0.337
0,224
0.346
04:00
0,470
0.365
0.302
0.394
0:394
0.577
0.387
0:600
0.7.9
0851
00899
0,775
3.947
3,993
5.16
4113
4,300
0,360
0,386
0:020
0.439
0.243
0.252
0.654
0.245
0.295
0,669
0.004
0.721
0,770
0,714
0.885
CALF
MSE
0.350
0.314
0.250
1.231
1,227
1.508
1.177
1,286
0.178
0.232
01:200
0,216
0.307
0.102
0.210
0.300
0.365
01244
0.315
0.335
0.341
0.312
0.325
01964
1.713
1.886
1.491
67-27
69.008
744631
76.376
0,782
0.814
0.776
0.991
0.906
0.174
0.176
0.345
0.993
0.222
0.957
1.111
1.300
1.098
8,30
67
MAE
0.245
0.530
0.359
0.305
0,907
0,914
0,880
0.895
0.999
0.361
0,379
0,39K
0,396
0.314
0.253
0.359
0,100
0
0,374
0,387
0.405
0.401
0.395
0,398
0.727
0.865
0.917
0.335
3.992
4.004
4.212
4
4
0.37
0:21
0.65
0,505
0,444
0.316
0.311
0.328
0,522
0.330
0.656
0.609
0.231
0.790
0.117
0.585
Time V.N
MSE
0.44
01271
0.522
01238
1.118
1.181
1.179
1200
1.
0.204
0.223
01244
0.245
01229
0.14
0.227
0.30M
0.290
0.540
0.304
0327
0.
0.333
0.500
1.19
1557
1.702
1,40
967
70.228
75.30
80 767
73.21
0.733
0322
0.96
1066
0300
0330
02/2
0222
0.222
021
0.555
01992
1959
1281
1051
7961
MAB
0.25
0. 0.320
0,350
00300
0.897
0.585
0.385
0.330
0.366
0.576
0.568
0.222
0.344
0418
0.480
0 0.568
0.402
0-471
0.391
0.55 0.306
0612
0
0
0.767
838
4.46
429
4.07
0.370
0.099
0.559
0.774
0.428
0.226
0.42
0.240
0.24
0.208
0.650
0.717
0.763
0.796
0.856
Multimooka
TATS
0.
MNE
01187
0.24
0.290
0:215
1.194
1.170
1.17
1N
0.196
0.214
0:315
0.005
0.216
0.309
0.391
0.255
0,307
0.334
0.312
0.302
0.319
0,009
1.523
1
3.35
65
71,000
74.094
776056
72
0.753
0.65
0.991
0.9
0.164
0.178
0.865
0.1 889
0.179
0
1.054
1.3066
10577
7,808
0.1
MAE
0.257
0.208
0,342
0.208
0.895
0.009
0,885
0.576
0.889
0,547
0.357
0.36
0.244
0.362
0-118
0.296
0.400
0.001
0-21
0
0-00
0346
0,099
0.99
359
3,955
4316
4068
0.578
0505
0.646
0-70
0.50
0.264
0.256
0.257
0.368
0.261
0,686
0,679
0,251
0.47
07711
0,662
CIPT-MITS
0.3
MNL
08-220
0.301
0:225
1.09
1.205
1.552
1.182
0.187
0.202
0.37
3.
0.111
0.232
0. 0.308
0.399
8.262
0.340
13336
00.299
0.325
1
1.757
1.484
7KI
01
78
71.487
0.718
0.962
0.929
1.095
0.192
0.19K
0.204
0.218
0.208
0.897
00:655
14036
1.046
0.986
7,726
68
MAL
0.342
0.315
0,338
0.294
0.301
0.833
0.891
0.344
0.370
0.77
0.343
0.355
0-423
0.484
0.395
0.001
03399
00385
0 0.303
0.49
0,75
0
0.878
0.777
3.88
4003
4055
4.19
4.047
0.374
02559
0.463
0.475
0.278
0.362
0.264
0,260
0, 0.366
0,650
0.221
0.269
0.708
0,89 0,960
GPE4TS
MSE
0.355
0.258
0.391
0.233
1.207
1.171
1.184
0.55
0.313
022
022
0.217
0.11
0.23
0.314
0
0.250
0.520
0.400
0.3
830
0.22
1.41
1
3.41
4226
279
72 28388
76 9KS
76
0.77
0555
0.90
0.917
0. 199
0.2044
02.0
0
0.206
0,902
1.006
3222
1.048
7668
01.
68
MAB
0.322
0.379
0.363
0.924
0.955
0.947
0.944
0.399
0.425
0.47
424
0.202
0 0.300
R.412
0:40
R-22
0.429
0.42
766
1030
965
4.55
4,887
655
4.408
0. 400
0.424
0.512
0.500
0-418
0.386
0. 1.3992
0.673
1.759
0.839
0.745
0,950
Re0
MSE
0.157
0.262
0.326
01246
1.202
1435
1.2%
1.345
1.542
0.222
0279
02275
0.294
11275
0.116
0.204
0,292
0,572
0,246
0.321
0.317
03370
0.341
0.337
1.280
1,922
1990
100
1.788
71
23.405
78.363
83
76.547
0.789
0540
1.008
141
0.277
0.325
0.290
0,308
0,300
0,220
199
1.120
1.347
1,9%
Text eat--ehhaoodd
MAS
0.263
0.310
0.220
0.389
0.316
0.898
0.881
0.38
0.367
0. 6.70
0.22
0.343
0-13
0.23
0, 362
0.59
0600
0:00
0:385
0.30 307
0.0
0324
0.76
4
400
2.99
4.17
0.300
0411
0.45
0.477
0.42
0.242
0.23
0.237
0.24 0446
0.299
0.6
0.215
0.26
0.865
PachhTST
MSE
0.180
0.224
0.318
0,212
1.306
1,71
1.17%
0.199
0.216
0.224
0.219
0.105
0.214
0.308
0.35
0.25
0.30
0.33
330
0.500
0.3
0.0%
1.53
1
1.30
06
093
76
72.23
0.750
0.348
0.978
1.09
0.00
0.163
0.168
0.738
0.188
0.174
0.258
1-03
1.243
1,066
7,800
MAB
0.265
0.336
0.353
0.310
0.857
0,63
0
0335
03
0.354
0.363
0.373
0.779
03367
0:211
0.35
0.423
02
0.333
0136
0390
0-461
0.379
0.300
0,600
LON0
020
0.55
4003
120
4154
0.300
0.436
0600
063
0:24
0.2
0.243
0223
0.638
0.32
0.731
0.70
0.811
iTrinsf
MSL
053
195
0.363
0.35 305
229
1.117
111
1.1
0. 19999
0.208
0.215
124
0,208
0,321
0.288
0.265
0.26
0. C20
0:363
0.278
3.02
132
1.721
1.3
194
21
507
74
063
0.887
1.2
0 16R
0 185
0 186
0 196
0 164
0.00
1.016
1.044
1051
1.0%
8,0
69
25
MAL
0.285
0.524
0.383
0.322
0.925
0.91
0.930
0.92
0.599
0.402
0.625
0,111
0.255
0.334
0-13
0.407
0.409
0.13
0.400
0.228
0.423
0.760
1065
1
0.975
4,51
4,398
4.712
4.473
0.404
0.463
0.517
0.55%
0.104
0.385
0.572
0.585
0.587
0.302
0,675
0:711
0.760
0.838
0.745
0.951
R4n
MSE
0.154
01
0.252
0.101
012236
1.298
1.360
1.298
1.309
0223
0.553
0.22
0313
0.506
02375
0.254
0:326
0.211
0.369
0.342
0.330
1.297
1.773
2075
2
183
521
74.755
93
27
0.796
0.507
1009
0.395
01274
0,292
0,300
02
0.923
1.0
1.144
1347
1.099
R376
71
MAL
0.254
0:320
0.351
0
506
0.577
0.585
0.344
0.356
0.309
0.32
0 0:363
0:25
0.339
0305
0224
0:563
0.0
0/00
0:011
0:31
0 00005
0,600
0.671
0.997
0.504
3
3
4.81
4.254
4.00
0.363
0416
0.77
0.44
0.425
0.238
0220
02239
0239
0.24
0.68%
0.723
0930
0.751
0.961
7S only
PwchTST
MSE
0.551
0.256
0.3 3.09
0:22X
1.197
1.
11144
0.19
0.303
0.218
0.229
0:210
0.107
0311
0.208
0.282
9.350
0.207
0.220
P55
0:501
0.317
1.0%
400
1615
1:706
443
66
090922
74079
77815
720027
0.724
0.600
0.950
163
0.944
0.100
0.17
0.181
0 1:5
0.1%6
0.594
03907
1.057
663
1145
7.791
Temmereemer
MAB
0.254
0.296
0.331
0,352
0.300
0,860
08844
00665
0,344
0.360
04
0.778
0.258
0.35%
0.399
0,408
0.302
0.39
080
0.74
0R7
0.99
0.33
4076
4.
4.03
4217
0.376
0.491
0.170
0.663
0235
0222
0237
0.250
0.228
083
0723
077
0,00
0887
MSE
0.128
0.254
0, 3.0
1.111
1.131
15555
0. 19
204
16264
222
0.121
0.232
0.329
00405
269
02 232
12204
0.276
1.261
1617
1.723
1559
22.230
75-50
M2 644
03
780
0.897
1014
153
961
01 176
0. 167
184
1.223
1.403
1.08
1.11
1.251
8 106
69
0.
0
mineriial branch
MAL
0.256
0:317
0.356
0.304
0.547
0.561
045
0.338
0.310
0.367
0.377
0.35%
0.227
0.325
0/406
0.440
0.500
0.38
0. 1.396
0.374
0.3
0
0.70
0.10
0:733
4.7%%
0.995
0.42
0.57
0,400
0.236
0240
0.241
0.24
0.655
0.722
0.774
0.711
0,811
MSS
0:221
301
0.214
000
1,974
1I1
L022
0 L
193
0.222
0,313
0. 101
0 100
01300
0.329
0,232
0.38
0.273
0.30 19388
0.256
0.274
380
450
260
45 455
23 680S
70 700
0.704
0,305
0,592
0.311
170
0.177
072
0.543
LOTG
T224
0.99%
0.
0.
03
0.
MAE
0.245
0,519
0,355
0.302
0
0.837
0.336
0.8445
0,3440
0.331
0.346
0.374
11,55
0.218
0.314
0,303
0.425
0.345
0.384
0.37%
0.334
0,574
0,300
0.5%%
0:750
0.2012
0,714
3
3,838
4155
3.377
0.340
0,575
0.402
0,227
0
0,234
0.2.9
0.2.2
0,602
0.710
0
0.716
0,820
ViT
MSE
0. 235
288
1.239
1
1,025
1.888
1,478
61,76
1.5
11:30
0..91
0.184
17.778
0.337
11:222
1L.73
0.270
31
15255
06
1.283
1.429
1.305
254
18.175
NW.117
03675
(L774
0.844
(L.25
0.004
0.155
0.173
0
169
1L998
080
9668
7.224
64
75
10
12
avg
12
6
10
12
agg
12
24
36
48
avg
48
96
192
3356
12
24
36
28
6.
12
agg
6
&
10
avg
&
10
12
avg
48
192
336
avg
Calegory
Medels
Metric
Agriculture
Climate
Eccosony
Energ
Health
Seandry
Sunial SuniallCooo Gload
Traflic
Weather
29


Reviews:
[{"review_id": "suaVJ1uk7m", "reviewer": "Reviewer_Wk2i", "summary": "The paper proposes VoT (Value of Text) a multimodal model to process text + numerical data for TSF. It uses an event-driven prediction branch and a numerical prediction branch to obtain branch wise predictions. event-driven prediction branch processes exogenous text data and uses retrieval to get relevant time-series data for historical in context learning (HIC). The numerical prediction branch aligns the time series with endogenous text that describes the time series. These branch wise predictions are then separated by 3 frequency bands (low, middle and high). The adaptive frequency fusion module (AFF) performs a linear combination at each band to then obtain to obtain the final forecast of the time series. The authors perform ablations of each module as well as perform experiments across 10 domains (from the MM-TSFLib benchmark + a new weather dataset) and 8 baselines to show superiority of the proposed method.", "questions": "- How do the authors ensure in equation 7 that $E^{tr}$ and  $E^{se}$ extract trend and seasonality? What is enforcing this behaviour (the loss, prompt or something else)? This should be made clear in section 3.4.1. To me it does not seem like this is being enforced, in which case $Q^{tr}$ and $Q^{se}$ are essentially different query heads, capturing different information?\n\n- Table 6 is quite large and bolding numbers would be beneficial to the reader.\n\n - Exactly how is the HIC knowledge base constructed per split? During inference at time t, are you guaranteed to retrieve only corrections derived from training windows strictly before t? \n\n- How are the initial band boundaries chosen per dataset?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. I like that the authors performed ablation on every module (ETA, AFF, HIC, Event branch), and appreciate the authors giving some insights rather than just presenting numbers. For instance, in section 4.3.1, the authors note that \"removing HIC results in worse performance than removing the entire event-driven branch (w/o Event), suggesting that unguided LLM reasoning can be more detrimental than no reasoning at all.\", which I think is a useful insight.\n\n2. The pipeline of using LLMs to process exogenous text data to create summaries and time series, correcting the reasoning process to keep the correct samples and using a retrieval mechanism for historical in context learning seems a bit over-engineered. However, this is still an interesting idea, is novel (to the best of my knowledge), and helps improve performance.\n\n3. The model achieves SOTA performance over the unimodal and multimodal baselines on all tasks.", "weaknesses": "1. The main weakness of the paper stems from the risk of temporal leakage in HIC module: It is crucial that the knowledge base never indexes summaries/corrections from future windows relative to the test horizon. The paper should spell out strict time-based splits for KB construction and retrieval, but HIC description lacks an explicit leakage guarantee.\n\n2. The final prediction of the model is a linear combination of band-specific frequency components $\\mathcal{F}^b_{*}$ between the exogenous (event-drive) and endogenous (numerical) branches. Why is this so? It seems quite reasonable to believe that the representation of these branches would have complex non-linear interactions. The paper would benefit from an ablation that models the final prediction as a nonlinear function instead (for example, MLP or cross attention).\n\n\nMinor: The authors have provided a link to their code, but most files are not viewable (I get an error: \"The requested file is not found\").", "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": "fDAfguZpgj", "reviewer": "Reviewer_Se43", "summary": "The paper introduces VoT (Value of Text), a multimodal time-series forecasting framework that integrates exogenous and endogenous text using a dual-branch architecture. The event-driven branch performs generative reasoning over textual events via Historical In-Context Learning (HIC), while the numerical branch aligns representations through Endogenous Text Alignment (ETA) and fuses predictions with Adaptive Frequency Fusion (AFF).", "questions": "- Can the authors provide a breakdown of performance during event vs. non-event intervals to demonstrate that “event-driven reasoning” indeed contributes selectively?\n- How does HIC perform when no close historical correction exists (e.g., unseen event types)? Is retrieval robust to domain shift?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clear motivation that textual signals carry event-driven information complementary to numerical dynamics.\n- Technically coherent pipeline combining LLM-based reasoning and representation alignment.", "weaknesses": "- The Event-Driven Reasoning and Historical In-Context Learning (HIC) components are interesting, but it remains somewhat unclear how much of the improvement truly comes from the reasoning process itself. Could the authors clarify whether HIC’s benefit persists when compared to simpler retrieval or embedding-based baselines? Also, since the reasoning pipeline involves curated summaries and correction prompts, how robust is it under domain shift or when applied without instruction-tuned LLMs?\n- The averaged results suggest strong overall performance, but they do not reveal when text contributes most. Have the authors examined per-event or per-regime behavior—for instance, during shocks, sudden changes, or stable intervals? Such an analysis might clarify whether the textual reasoning module adapts dynamically to event intensity or merely improves overall fit.\n- The Endogenous Text Alignment (ETA) and Adaptive Frequency Fusion (AFF) appear to rely on manually chosen decompositions (trend/seasonal, low/mid/high frequency). Are these partitions fixed, or are they optimized jointly with the forecasting backbone? If they are heuristic, have the authors tested sensitivity to these design choices or considered end-to-end learning variants?\n- Given that the system depends on multiple LLMs and retrieval modules, can the authors discuss its computational footprint and inference latency? Is the framework feasible for continuous or real-time forecasting, or is it primarily an offline research prototype?", "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": "ZG6kc1QYz9", "reviewer": "Reviewer_jn6c", "summary": "The paper proposes VoT (Unlocking the Value of Text), a framework that injects real-world textual context (e.g., news, reports, policies) into time-series forecasting through event-driven reasoning and multi-level alignment. VoT runs two prediction branches in parallel: (1) an event-driven LLM branch that converts external text into numeric forecasts via a three-stage pipeline (template → summary → reasoning) and a Historical In-Context (HIC) mechanism that retrieves past correction examples to reduce recurring errors; and (2) a numerical branch that aligns time-series embeddings with endogenous text describing trend/seasonality using ETA (representation-level alignment). Final predictions are fused in the frequency domain with AFF, which learns band-specific weights (low/mid/high) to exploit each branch’s strengths.\nContributions\n- A principled event-to-forecast pipeline that turns text reasoning into numeric predictions, regularized by HIC.\n- ETA: fine-grained representation alignment between TS embeddings and textual descriptions (trend/seasonality).\n- AFF: prediction-level alignment via frequency-band fusion of event and numerical branches.\n- Extensive benchmarks (10 domains) showing consistent gains over strong time-series and multimodal baselines, especially for event-sensitive regimes.", "questions": "- Could you add a baseline that performs ETA via simple TS–Text contrastive learning without trend/seasonal decomposition? Showing a consistent gap in favor of your decomposition-based ETA would strengthen the core claim.\n- Why text-driven decomposition? Compared to standard time-series-based TS decomposition [1,2] applied to (H_{\\text{ts}}), what concrete advantage does using endogenous text with TS–Text attention to produce (TS_Text_trend) and (TS_Text_seasonal) provide? Please quantify where text-guided components outperform classic decompositions.\n- In Figure 6, the differences between the TS-only (pink) and event-driven (green) branches are not very clear across high- and low-frequency ranges. To make their roles more distinct, it might help to report a quantitative evaluation or show a case with a pronounced regime shift where the two branches diverge more visibly.\n\n[1] Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting, NeurIPS21.\n[2] Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling, ICLR24.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "Originality: Proposes a dual-branch framework that combines Event-driven Reasoning from exogenous text with Multi-level Alignment (ETA for representation-level, AFF for prediction-level). The Historical In-Context Learning (HIC) idea—retrieving corrected past reasoning to guide current predictions—is a creative reuse of prior errors as in-context examples.", "weaknesses": "- The endogenous text appears to restate information already present in the series; the paper does not clearly justify why ETA (textualizing trend/seasonal components and aligning them) adds value beyond standard time–series decomposition and representation learning.\n-  The computation of $Y_{\\text{num}}$ is under-specified. It is unclear whether the forward pass at inference consumes only $H_{\\text{ts}}$ or also the aligned textual features (e.g., $Z_{\\mathrm{tr}}, Z_{\\mathrm{se}}$).\n- The construction and maintenance of the Knowledge Base $K$ are not operationalized: if an entry is stored per training window, $|K|$ may become very large. The paper does not detail pruning, refresh policies, or retrieval complexity/latency.", "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": "VbDbsXu0nJ", "reviewer": "Reviewer_UX4Y", "summary": "The paper presents VoT, a dual-branch framework for multimodal time-series forecasting. One branch performs event-driven reasoning over exogenous text through a three-stage pipeline enhanced by Historical In-Context Learning (HIC). The second branch conducts Endogenous Text Alignment (ETA) by converting statistics of the target series into textual descriptors and aligning them with numeric representations via contrastive learning. The two branches are combined using Adaptive Frequency Fusion (AFF), which learns frequency-band weights so that low-frequency, event-level effects and high-frequency fluctuations are fused coherently. Experiments on multiple datasets report consistent gains over strong baselines, with ablations attributing benefits to HIC, ETA, and AFF.", "questions": "- What are wall-clock training and inference costs for the event pipeline and HIC retrieval, and how do they scale with text volume?\n- How does VoT compare with (a) a small gated residual that can down-weight text channels and (b) a light cross-attention/FiLM block with similar parameter counts?\n- How sensitive are results to the choice of frequency bands, and do learned fusion weights transfer across datasets or shifts?\n- What is the behaviour when exogenous text is noisy, or sparse, and is there a fallback to ETA-only or TS-only prediction?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- Separating event-reasoning over exogenous text from numerical modelling, then fusing by learned frequency bands, is well motivated and clearly presented.\n- The HIC retrieval strategy is a reasonable way to reuse corrected reasoning traces without fine-tuning.\n- Strong empirical results span numerous datasets and include ablations that remove or modify key components.", "weaknesses": "- The generative pipeline plus HIC retrieval likely increases latency and cost; wall-clock measurements and throughput are not reported.\n- The evaluation omits parameter-matched alternatives such as a gated residual or a lightweight cross-attention/FiLM plug-in, making it difficult to isolate architectural merit.\n- Split protocols are not described in sufficient detail to exclude future-revealing or outcome-summarising language at time t.\n- The stability of learned frequency weights under distribution shift and the sensitivity to band partitioning remain unclear.", "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)