Papers › FITS: Modeling Time Series with $10k$ Parameters

FITS: Modeling Time Series with $10k$ Parameters

6 Jul 2023arXiv:2307.03756archive 2025-07-28

Zhijian Xu, Ailing Zeng, Qiang Xu

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}

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vewoxic/fits officialmentioned in paperpytorchApache-2.0 report

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Tasks

Anomaly DetectionTime SeriesTime Series AnalysisTime Series Forecasting

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Time Series Forecasting ETTh1 (336) Multivariate FITS MSE 0.427 #25 of 72 Archive leaderboard report

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