Towards Time Series Reasoning with LLMs
<|reference_start|>Towards Time Series Reasoning with LLMs: 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.<|reference_end|>
@article{chow2024towards,
    title={Towards Time Series Reasoning with LLMs},
    author={Winnie Chow, Lauren Gardiner, Haraldur T. Hallgr'imsson, Maxwell A.
  Xu, Shirley You Ren},
    journal={arXiv preprint arXiv:2409.11376},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11376},
    primaryClass={cs.LG}
}