Inferring Events from Time Series using Language Models

ACL ARR 2025 May Submission841 Authors

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://anonymous.4open.science/r/Event_Infer-1B20/

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: Resources and Evaluation, Language Modeling, NLP Applications, Multimodality and Language Grounding to Vision, Robotics and Beyond, Time Series

Contribution Types: Data resources, Data analysis

Languages Studied: English

Submission Number: 841