Toward Reasoning-Centric Time-Series Analysis
This work addresses the problem of improving interpretability and adaptability in time series analysis for applications in dynamic real-world environments, though it appears incremental as it builds on existing LLM methods.
The paper argues that time series analysis should shift from pattern recognition to a reasoning-centric approach using Large Language Models (LLMs) to uncover causal structures and improve explainability in real-world settings, aiming for more transparent and context-aware insights.
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.