Unlocking the Value of Text: Event-Driven Reasoning and Multi-Level Alignment for Time Series Forecasting

2026-03-16

AI Summary

This work addresses the limitation of existing time series forecasting methods, which predominantly focus on numerical data and struggle to effectively incorporate accompanying textual information, thereby constraining their ability to model complex real-world scenarios. To overcome this, the authors propose a multimodal fusion framework that leverages event-driven reasoning and historical context learning to guide large language models in semantic inference. The framework introduces an endogenous text alignment mechanism and an adaptive frequency-domain fusion strategy to achieve deep integration of textual information at both representation and prediction levels. Extensive experiments across ten real-world datasets spanning diverse domains demonstrate that the proposed method significantly outperforms current state-of-the-art approaches, confirming that effective utilization of textual cues can substantially enhance time series forecasting performance.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

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.

Problem

• time series forecasting
• multimodal
• text utilization
• event-driven reasoning
• multi-level alignment

Innovation

• Event-driven Reasoning
• Multi-level Alignment
• Historical In-context Learning
• Adaptive Frequency Fusion
• Multimodal Time Series Forecasting