ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning
Summary: ChatTS is the first MLLM treating multivariate time series as a native modality for alignment and reasoning. Attribute-based synthetic series and Time Series Evol-Instruct enable exclusive synthetic-data fine-tuning, yielding strong gains over GPT-4o and LLM baselines. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Zhe Xie (Beijing Institute of Technology; Tsinghua University)
- 2. Zeyan Li (ByteDance)
- 3. Xiao He (ByteDance)
- 4. Longlong Xu (Beijing Institute of Technology; Tsinghua University)
- 5. Xidao Wen (BizSeer)
- 6. Tieying Zhang (ByteDance)
- 7. Jianjun Chen (ByteDance)
- 8. Rui Shi (ByteDance)
- 9. Dan Pei (Beijing Institute of Technology; Tsinghua University)
BibTeX Citation
@article{xie_vldb25,
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 = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {8},
pages = {2385--2398},
doi = {10.14778/3742728.3742735},
url = {https://doi.org/10.14778/3742728.3742735},
year = {2025}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,659 | D-Bot: Database Diagnosis System using Large Language Models | 2024 | VLDB | 9.9625133e-05 |
| 7,772 | OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting | 2023 | VLDB | 5.453953e-05 |
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