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# [VLDB' 25] ChatTS-14B Model
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<div style="display:flex;justify-content: center">
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<a href="https://github.com/NetmanAIOps/ChatTS"><img alt="github" src="https://img.shields.io/badge/
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<a href="https://arxiv.org/abs/2412.03104"><img alt="preprint" src="https://img.shields.io/static/v1?label=arXiv&message=2412.03104&color=B31B1B&logo=arXiv"></a>
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`ChatTS` focuses on **Understanding and Reasoning** about time series, much like what vision/video/audio-MLLMs do.
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This repo provides code, datasets and model for `ChatTS`: [ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning](https://arxiv.org/pdf/2412.03104).
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Here is an example of a ChatTS application, which allows users to interact with a LLM to understand and reason about time series data:
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# [VLDB' 25] ChatTS-14B Model
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<div style="display:flex;justify-content: center">
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<a href="https://github.com/NetmanAIOps/ChatTS"><img alt="github" src="https://img.shields.io/badge/Code-GitHub-blue"></a>
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<a href="https://arxiv.org/abs/2412.03104"><img alt="preprint" src="https://img.shields.io/static/v1?label=arXiv&message=2412.03104&color=B31B1B&logo=arXiv"></a>
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</div>
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`ChatTS` focuses on **Understanding and Reasoning** about time series, much like what vision/video/audio-MLLMs do.
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This repo provides code, datasets and model for `ChatTS`: [ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning](https://arxiv.org/pdf/2412.03104).
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`ChatTS` features native support for multi-variate time series data with any length and range of values. With `ChatTS`, you can easily understand and reason about both the **shape** features and **value** features in the time series.
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`ChatTS` can also be integrated into existing LLM pipelines for more time series-related applications, leveraging existing inference frameworks such as `vLLMs`.
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Here is an example of a ChatTS application, which allows users to interact with a LLM to understand and reason about time series data:
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