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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.11376 |
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| _version_ | 1866929615627354112 |
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| author | Chow, Winnie Gardiner, Lauren Hallgrímsson, Haraldur T. Xu, Maxwell A. Ren, Shirley You |
| author_facet | Chow, Winnie Gardiner, Lauren Hallgrímsson, Haraldur T. Xu, Maxwell A. Ren, Shirley You |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11376 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Time Series Reasoning with LLMs Chow, Winnie Gardiner, Lauren Hallgrímsson, Haraldur T. Xu, Maxwell A. Ren, Shirley You Machine Learning 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. |
| title | Towards Time Series Reasoning with LLMs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.11376 |