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FITS: Modeling Time Series with 10k Parameters

2023-07-06Code Available2· sign in to hype

Zhijian Xu, Ailing Zeng, Qiang Xu

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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

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
ETTh1 (336) MultivariateFITSMSE0.43Unverified

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