  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}
