FITS: Modeling Time Series with 10k Parameters
Zhijian Xu, Ailing Zeng, Qiang Xu
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/vewoxic/fitsOfficialIn paperpytorch★ 229
- github.com/WenjieDu/PyPOTSpytorch★ 1,970
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 code is available in: https://github.com/VEWOXIC/FITS
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| ETTh1 (336) Multivariate | FITS | MSE | 0.43 | — | Unverified |