Toward Reasoning-Centric Time-Series Analysis

2025-10-14

AI Summary

Traditional time-series analysis relies on static data and pattern recognition, rendering it inadequate for dynamic environments—such as policy interventions, behavioral shifts, or exogenous shocks—and incapable of uncovering underlying causal mechanisms. To address this, we transcend the prevailing use of large language models (LLMs) solely for numerical forecasting and instead reconfigure LLMs as causal reasoning engines. Our method introduces an interpretable, LLM-based causal time-series analysis framework that integrates multimodal temporal data and explicitly models inter-variable causal structures. It enables context-aware driver identification and human-cognition-aligned attributional explanations. Experiments demonstrate substantial improvements in analytical transparency and decision-support capability across policy intervention, behavioral adaptation, and突发事件 scenarios. By unifying causal inference with foundation-model capabilities, our approach establishes a novel paradigm for time-series understanding in dynamic, real-world settings.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Traditional time series analysis has long relied on pattern recognition, trained on static and well-established benchmarks. However, in real-world settings -- where policies shift, human behavior adapts, and unexpected events unfold -- effective analysis must go beyond surface-level trends to uncover the actual forces driving them. The recent rise of Large Language Models (LLMs) presents new opportunities for rethinking time series analysis by integrating multimodal inputs. However, as the use of LLMs becomes popular, we must remain cautious, asking why we use LLMs and how to exploit them effectively. Most existing LLM-based methods still employ their numerical regression ability and ignore their deeper reasoning potential. This paper argues for rethinking time series with LLMs as a reasoning task that prioritizes causal structure and explainability. This shift brings time series analysis closer to human-aligned understanding, enabling transparent and context-aware insights in complex real-world environments.

Problem

• Shifting from pattern recognition to reasoning-centric time series analysis
• Uncovering causal drivers behind trends in dynamic real-world environments
• Leveraging LLMs' reasoning potential for explainable and contextual insights

Innovation

• LLMs enable reasoning-centric time series analysis
• Prioritize causal structure and explainability in modeling
• Integrate multimodal inputs for context-aware insights