FITS: Modeling Time Series with 10k Parameters

Zhijian Xu, Ailing Zeng, Qiang Xu

Keywords: Time series analysis, Time series forecasting, Complex-valued neural network

Abstract:

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 anonymous code repo is available in: \url{https://anonymous.4open.science/r/FITS}

Submission Number: 11163