Towards Time Series Reasoning with LLMs
This addresses the gap in applying LLMs to time-series reasoning, which could benefit fields like finance, healthcare, and IoT, though it builds incrementally on existing multi-modal LLM techniques.
The paper tackles the problem of enabling large language models to perform natural language reasoning on time-series data, proposing a multi-modal approach that combines a time-series encoder with chain-of-thought fine-tuning. The result is a model that outperforms GPT-4o on zero-shot reasoning tasks across various domains.
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.