Beyond Forecasting: Compositional Time Series Reasoning for End-to-End Task Execution

Wen Ye, Yizhou Zhang, Wei Yang, Lumingyuan Tang, Defu Cao, Jie Cai, Yan Liu

Keywords: Time Series, LLM, Reasoning

TL;DR: We developed a dataset and a method for compositional time series reasoning

Abstract:

In recent decades, there have been substantial advances in time series models and benchmarks across various individual tasks, such as time series forecasting, classification, and anomaly detection.
Meanwhile, compositional reasoning in time series prevalent in real-world applications (e.g., decision-making and compositional question answering) is in great demand. Unlike simple tasks that primarily focus on predictive accuracy, compositional reasoning emphasizes the synthesis of diverse information from both time series data and various domain knowledge, making it distinct and extremely more challenging. In this paper, we introduce Compositional Time Series Reasoning, a new task of handling intricate multistep reasoning tasks from time series data. Specifically, this new task focuses on various question instances requiring structural and compositional reasoning abilities on time series data, such as decision-making and compositional question answering.
As an initial attempt to tackle this novel task, we developed \modelname, a program-aided approach that utilizes large language model (LLM) to decompose a complex task into steps of programs that leverage existing time series models and numerical subroutines. Through a comprehensive set of experiments, we demonstrate that our simple but effective \modelnamespace outperforms existing standalone reasoning approaches. These promising results indicate potential opportunities in the new task of time series reasoning and highlight the need for further research.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 3932