EasyTS: The Express Lane to Long Time Series Forecasting


  • Tiancheng Zhang Tianjin University
  • Shaoyuan Huang Tianjin University
  • Cheng Zhang Tianjin University of Finance and Economics
  • Xiaofei Wang Tianjin University
  • Wenyu Wang Paiou Cloud Computing (Shanghai) Co., Ltd, Shanghai, China




Software and testing tools for developing AI technologies


Responding to the escalating interest in long-term forecasting within the industry, we introduce EasyTS, a comprehensive toolkit engineered to streamline data collection, analysis, and model creation procedures. EasyTS acts as a unified solution, driving progress in long-term time series forecasting. The platform provides effortless access to various time series datasets, including a newly open-sourced multi-scenario dataset in the electricity domain. Integrated visualization and analysis tools help unveil inherent data features and relationships. EasyTS facilitates a user-friendly model validation approach with versatile evaluation criteria. This toolkit allows researchers to compare their models proficiently against renowned benchmarks. With our ongoing commitment to expanding our dataset collection and enhancing toolkit functionalities, we aspire to contribute significantly to the time series forecasting domain. Code is available at this repository: https://github.com/EdgeBigBang/EasyTS.git.




How to Cite

Zhang, T., Huang, S., Zhang, C., Wang, X., & Wang, W. (2024). EasyTS: The Express Lane to Long Time Series Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23853-23855. https://doi.org/10.1609/aaai.v38i21.30587