Multi-modal large language models (MLLMs) have enabled numerous advances in understanding and reasoning in domains like vision, but we have not yet seen this broad success for time-series. Although prior works on time-series MLLMs have shown promising performance in time-series forecasting, very few works show how an LLM could be used for time-series reasoning in natural language. We propose a novel multi-modal time-series LLM approach that learns generalizable information across various domains with powerful zero-shot performance. First, we train a lightweight time-series encoder on top of an LLM to directly extract time-series information. Then, we fine-tune our model with chain-of-thought augmented time-series tasks to encourage the model to generate reasoning paths. We show that our model learns a latent representation that reflects specific time-series features (e.g. slope, frequency), as well as outperforming GPT-4o on a set of zero-shot reasoning tasks on a variety of domains.
@article{arxiv.2409.11376,
title = {Towards Time Series Reasoning with LLMs},
author = {Winnie Chow and Lauren Gardiner and Haraldur T. Hallgrímsson and Maxwell A. Xu and Shirley You Ren},
journal= {arXiv preprint arXiv:2409.11376},
year = {2024}
}
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Oral Presentation at 2024 NeurIPS Workshop on Time Series in the Age of Large Models