Inferring Events from Time Series using Language Models

• URL: http://arxiv.org/abs/2503.14190v2
• Date: Fri, 23 May 2025 00:58:52 GMT
• Title: Inferring Events from Time Series using Language Models
• Authors: Mingtian Tan, Mike A. Merrill, Zack Gottesman, Tim Althoff, David Evans, Tom Hartvigsen,
• Abstract summary: Time series data measure how environments change over time and drive decision-making in critical domains like finance and healthcare.<n>We conduct the first study of whether Large Language Models (LLMs) can infer events described with natural language from time series data.<n>Several current LLMs demonstrate promising abilities, with OpenAI's o1 performing the best but with DS-R1-distill-Qwen-32B outperforming proprietary models such as GPT-4o.
• Score: 13.414101942484582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series data measure how environments change over time and drive decision-making in critical domains like finance and healthcare. A common goal in analyzing time series data is to understand the underlying events that cause the observed variations. We conduct the first study of whether Large Language Models (LLMs) can infer events described with natural language from time series data. We evaluate 18 LLMs on a task to match event sequences with real-valued time series data using a new benchmark we develop using sports data. Several current LLMs demonstrate promising abilities, with OpenAI's o1 performing the best but with DS-R1-distill-Qwen-32B outperforming proprietary models such as GPT-4o. From insights derived from analyzing reasoning failures, we also find clear avenues to improve performance. By applying post-training optimizations, i.e., distillation and self-improvement, we significantly enhance the performance of the Qwen2.5 1.5B, achieving results second only to o1. All resources needed to reproduce our work are available: https://github.com/BennyTMT/GAMETime

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