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

cs.AIMar 16, 2026

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

Organizations: East China Normal University

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.

Figures & tables

Figure 1: Unemployment rate time series ( Liu et al., 2024a ) (1970-2020). While certain patterns ( pink ) exhibit predictable temporal regularities, abrupt changes ( green ) driven by external events necessitate the integration of textual information to complement numerical forecasting.

LLM Usage	Feature Extraction	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
	Sub-categories	VoT	GPT4TS	TimeLLM	TEST	CALF	CMIN	Maformer	DualTime	Time-MMD	TaTS	FNSPID	GPT4MTS	CiK
	Sub-categories		( 2025 )	( 2024b )	( 2024 )	( 2025a )	( 2023 )	( 2024 )	( 2024 )	( 2024a )	( 2026 )	( 2024 )	( 2024 )	( 2025 )
LLM Usage	Reasoning	✓	$\times$	$\times$	$\times$	$\times$	$\times$	$\times$	$\times$	$\times$	$\times$	$\times$	$\times$	$\times$
Text Type	Endogenous	✓	✓	✓	✓	✓	$\times$	$\times$	$\times$	$\times$	$\times$	$\times$	$\times$	✓
Text Type	Exogenous	✓	$\times$	$\times$	$\times$	$\times$	✓	✓	✓	✓	✓	✓	✓	✓

Table 1: Comparison of multimodal time series forecasting methods with text incorporation

Figure 2: The architecture of VoT. The event-driven branch processes exogenous text through a 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.

Figure 3: The processing procedure of the Historical In-Context Learning.

Figure 4: The processing procedure of the Endogenous Text Alignment (ETA).

Category			Time series-only						Text-enhanced
Models	VoT		PatchTST ( 2023 )		iTransformer ( 2024b )		RaFT ( 2025 )		PatchTST*		iTransformer*		RaFT*
Metric	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
Agriculture	0.209	0.302	0.228	0.303	0.220	0.308	0.226	0.322	0.232	0.316	0.229	0.310	0.246	0.333
Climate	1.078	0.840	1.184	0.888	1.135	0.865	1.289	0.926	1.178	0.887	1.117	0.858	1.342	0.944
Economy	0.201	0.353	0.210	0.363	0.222	0.378	0.265	0.411	0.219	0.370	0.213	0.367	0.275	0.420
Energy	0.222	0.343	0.250	0.363	0.269	0.382	0.254	0.367	0.253	0.365	0.265	0.383	0.246	0.360

Table 2: Forecasting results of time series-only and text-enhanced methods, and our method VoT. The best results are highlighted in bold , and the second-best results are

\underline{\text{underlined}}

Models	VoT		GPT4TS ( 2025 )		GPT4MTS ( 2024 )		TaTS ( 2026 )		Time-VLM ( 2025 )		CALF ( 2025a )
Metric	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
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	0.343	0.260	0.376	0.262	0.380	0.255	0.368	0.260	0.374	0.244	0.365
Environment	0.268	0.380	0.322	0.393	0.323	0.400	0.319	0.396	0.320	0.398	0.325	0.387

Table 3: Forecasting results of multimodal methods and VoT. The best results are highlighted in bold , and the second-best results are

\underline{\text{underlined}}

	Climate		Energy		SocialGood
Variant	MSE	MAE	MSE	MAE	MSE	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

Table 5: Ablation Study on Components of the Event-based Temporal Alignment (ETA)

Panel of Figure 6 — Figure 6: Frequency domain analysis of time series predictions (Social Good). (a) Original signals without frequency decomposition. (b)-(d) Frequency-filtered 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 )

Appendix figures & tables14 assets

Supplementary material from the paper’s appendix.

Appendix

Category	Method	Repository
Time Series Only	iTransformer	https://github.com/thuml/iTransformer
	PatchTST	https://github.com/yuqinie98/PatchTST
	RAFT	https://github.com/archon159/RAFT
Text-Enhanced (Time-MMD)	iTransformer*	Based on iTransformer with Time-MMD text integration
	PatchTST*	Based on PatchTST with Time-MMD text integration
	RAFT*	Based on RAFT with Time-MMD text integration

Appendix

Table 6: Implementation details and code repositories for baseline methods

Figure 7: Heat Map of MSE/MAE Values Based on Low-Frequency and High-Frequency Percentage Cutoffs. The x-axis represents the percentage cutoffs for the low-frequency range, varying from 10% to 40% of the low-frequency 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)

Branch / Module	Inference Time (s)
Event-driven Branch	5.9
Numerical Branch	0.002
AFF	0.0005

Appendix

Table 7: Inference Latency of Main Model Components

Dataset	Method	MSE	MAE
Climate	No Retrieval	1.151	0.877
	Retrieve TS Only	1.137	0.866
	Retrieve Summary Only	1.091	0.846
	Full HIC	1.078	0.840
SocialGood	No Retrieval	0.845	0.410
	Retrieve TS Only	0.853	0.421

Appendix

Table 8: Ablation Comparison of HIC with Simplified Retrieval Methods

Dataset	Retrieved Example by Similarity Rank	MSE	MAE
Energy	1st-most Similar Example	0.222	0.343
	10th-most Similar Example	0.229	0.348
	100th-most Similar Example	0.233	0.348
	No Retrieval	0.238	0.363
SocialGood	1st-most Similar Example	0.804	0.389
	10th-most Similar Example	0.807	0.408

Appendix

Table 9: Model Robustness to Imperfect Retrieved Examples

Dataset	Condition	MSE	MAE
Climate	Mask 10% (mask10)	1.099	0.850
	Mask 20% (mask20)	1.109	0.854
	Noise 10% (noise10)	1.098	0.850
	Noise 20% (noise20)	1.103	0.854
	Our Method (Original)	1.078	0.840
SocialGood	Mask 10% (mask10)	0.822	0.448

Appendix

Table 10: Robustness Test of the Model Against Noisy and Sparse Text Data

Dataset	Method	MSE	MAE
Climate	Gated-Residual	1.230	0.912
	Cross-Attention	1.202	0.893
	FiLM	1.211	0.897
	ETA	1.092	0.848
Energy	Gated-Residual	0.248	0.360
	Cross-Attention	0.254	0.369

Appendix

Table 11: Ablation Study on Temporal Alignment Methods

Figure 8: The visualization of the datasets

Dataset	Fusion Method	MSE	MAE
Climate	MLP	1.228	0.875
	Cross-Attention	1.768	1.055
	AFF	1.078	0.840
Energy	MLP	0.685	0.591
	Cross-Attention	0.430	0.502
	AFF	0.222	0.343

Appendix

Table 12: Performance Comparison of Fusion Methods on Multiple Datasets

Period	Branch	Energy		SocialGood
Period	Branch	MSE	MAE	MSE	MAE
Event Part	Event-driven Branch	0.402	0.450	3.080	0.979
	Numerical Branch	0.443	0.523	3.339	1.022
	Fusion Result	0.362	0.460	3.084	0.969
Non-Event Part	Event-driven Branch	0.266	0.362	0.116	0.270
	Numerical Branch	0.105	0.257	0.068	0.208

Appendix

Table 13: Prediction Performance Breakdown in Event and Non-Event Periods

Pred Length	Comparison	High Freq	Low Freq
8	Event-driven vs Ground Truth	0.155412	0.497763
8	Numerical vs Ground Truth	0.423805	0.458464
10	Event-driven vs Ground Truth	0.265945	0.402431
10	Numerical vs Ground Truth	0.349408	0.381150
12	Event-driven vs Ground Truth	0.273838	0.466686
12	Numerical vs Ground Truth	0.310766	0.436835

Appendix

Table 14: Pearson Correlation of Frequency Components (SocialGood)

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 as guidance for improved forecasting accuracy.

LLM	Energy		Social Good
LLM	MSE	MAE	MSE	MAE
DeepSeek-R1	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

Appendix

Table 15: Ablation study on different LLMs

Category						TS-only						Text-enhanced						Multimodal
Models		VoT		numerical branch		iTransformer		PatchTST		Raft		iTransformer*		PatchTST*		Raft*		GPT4TS		GPT4MTS		TATS		TimeVLM		CALF
Metric		MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
Agriculture	6	0.131	0.245	0.146	0.256	0.128	0.254	0.151	0.254	0.154	0.283	0.153	0.265	0.146	0.263	0.157	0.282	0.135	0.242	0.161	0.257	0.140	0.251	0.143	0.245	0.142	0.250
	8	0.182	0.286	0.188	0.287	0.200	0.296	0.197	0.287	0.194	0.297	0.195	0.296	0.189	0.310	0.237	0.344	0.198	0.284	0.207	0.288	0.187	0.282	0.215	0.287	0.195	0.285
	10	0.235	0.319	0.221	0.317	0.254	0.331	0.256	0.320	0.252	0.324	0.263	0.326	0.254	0.320	0.262	0.328	0.258	0.313	0.230	0.305	0.244	0.320	0.271	0.320	0.350	0.370
	12	0.288	0.355	0.301	0.356	0.300	0.352	0.309	0.351	0.301	0.383	0.305	0.352	0.338	0.369	0.326	0.379	0.291	0.338	0.301	0.342	0.290	0.350	0.322	0.359	0.314	0.355

Appendix

Table 16: Model Performance Comparison Across Different Categories

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