FITS: Modeling Time Series with 10k Parameters

By Zhijian Xu, Ailing Zeng, Qiang Xu. International Conference on Learning Representations, 2023

306 citations recorded by the source corpus.

In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis. Unlike existing models that directly process raw time-domain data, FITS operates on the principle that time series can be manipulated through interpolation in the complex frequency domain. By discarding high-frequency components with negligible impact on time series data, FITS achieves performance comparable to state-of-the-art models for time series forecasting and anomaly detection tasks, while having a remarkably compact size of only approximately $10k$ parameters. Such a lightweight model can be easily trained and deployed in edge devices, creating opportunities for various applications. The code is available in: \url{https://github.com/VEWOXIC/FITS}

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/afba7beaa7c66cfc72972eb2a569f13c7554bfef