This research paper proposes a new paradigm for time series analysis called "Compositional Time Series Reasoning," which focuses on solving complex, multi-step reasoning tasks using time series data. The authors argue that traditional time series analysis methods, primarily focused on individual tasks like forecasting, are insufficient for real-world applications that require integrating diverse information and domain knowledge.
The Problem: Existing time series models lack the reasoning abilities of LLMs, while LLMs struggle with the numerical intricacies of time series data. This gap hinders effective end-to-end execution of complex time series tasks.
The Proposed Solution: TS-Reasoner
To bridge this gap, the authors introduce TS-Reasoner, a program-aided reasoning approach that decomposes complex tasks into manageable subtasks represented as programs. TS-Reasoner leverages LLMs to understand user instructions and generate these programs, which are then executed using a toolbox of three module types:
Evaluation and Results:
The authors created a new dataset and evaluation framework focusing on three task categories: financial decision-making, compositional question answering (in finance and energy sectors), and causal mining. TS-Reasoner consistently outperformed baseline models (Chain of Thought and Chain of Thought + code) in terms of success rate, accuracy, and adherence to constraints.
Key Findings:
Significance:
This research highlights the potential of combining LLMs with program-based approaches for advanced time series analysis. It paves the way for developing more sophisticated systems capable of handling real-world complexities in fields like finance, energy, and climate science.
Limitations and Future Research:
The authors acknowledge limitations in handling extremely long reasoning chains and suggest exploring data from more diverse domains. Future research could focus on incorporating multimodal knowledge integration (e.g., tables, images) to further enhance compositional time series reasoning.
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by Wen Ye, Yizh... at arxiv.org 10-08-2024
https://arxiv.org/pdf/2410.04047.pdfDeeper Inquiries