| # ChatTS-14B Model | |
| `ChatTS` focuses on **Understanding and Reasoning** about time series, much like what vision/video/audio-MLLMs do. | |
| 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). | |
| Here is an example of a ChatTS application, which allows users to interact with a LLM to understand and reason about time series data: | |
|  | |
| ## Usage | |
| This model is fine-tuned on the QWen2.5-14B-Instruct (https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) model. For more usage details, please refer to the `README.md` in the ChatTS repository. | |
| ## Reference | |
| - QWen2.5-14B-Instruct (https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) | |
| - transformers (https://github.com/huggingface/transformers.git) | |
| - [ChatTS Paper](https://arxiv.org/pdf/2412.03104) | |
| ## License | |
| This model is licensed under the [Apache License 2.0](LICENSE). | |
| ## Cite | |
| ``` | |
| @article{xie2024chatts, | |
| title={ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning}, | |
| author={Xie, Zhe and Li, Zeyan and He, Xiao and Xu, Longlong and Wen, Xidao and Zhang, Tieying and Chen, Jianjun and Shi, Rui and Pei, Dan}, | |
| journal={arXiv preprint arXiv:2412.03104}, | |
| year={2024} | |
| } | |
| ``` | |