TSGAssist

TSGAssist is an interactive assistant that integrates the strengths of TSGBench and utilizes Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) for TSG recommendations and benchmarking 🤖📊

This work is currently under patent application. For more details, please visit the latest repository on Time Series Generation.

We are actively exploring industrial collaborations in time series analytics. Please feel free to reach out (yihao_ang AT comp.nus.edu.sg) if interested 🤝✨

Overview of TSGAssist

TSGAssist is an interactive assistant harnessing LLMs and RAG for time series generation recommendations and benchmarking.

• It offers multi-round personalized recommendations through a conversational interface that bridges the cognitive gap,
• It enables the direct application and instant evaluation of users' data, providing practical insights into the effectiveness of various methods.

References

Please consider citing our works if you use them in your research:

# TSGAssist
@article{ang2024tsgassist,
  title        = {TSGAssist: An Interactive Assistant Harnessing LLMs and RAG for Time Series Generation Recommendations and Benchmarking
},
  author       = {Ang, Yihao and Bao, Yifan and Huang, Qiang and Tung, Anthony KH and Huang, Zhiyong},
  journal      = {Proc. {VLDB} Endow.},
  volume       = {17},
  number       = {12},
  pages        = {4309--4312},
  year         = {2024}
}

# TSGBench
@article{ang2023tsgbench,
  title        = {TSGBench: Time Series Generation Benchmark},
  author       = {Ang, Yihao and Huang, Qiang and Bao, Yifan and Tung, Anthony KH and Huang, Zhiyong},
  journal      = {Proc. {VLDB} Endow.},
  volume       = {17},
  number       = {3},
  pages        = {305--318},
  year         = {2023}
}