FITS: Modeling Time Series with $10k$ Parameters

Published: 16 Jan 2024, Last Modified: 15 Mar 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
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Keywords: Time series analysis, Time series forecasting, Complex-valued neural network
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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, achieving performance comparable to state-of-the-art models for time series forecasting and anomaly detection tasks. Notably, FITS accomplishes this with a svelte profile of just about $10k$ parameters, making it ideally suited for edge devices and paving the way for a wide range of applications. The code is available for review at: \url{}.
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Primary Area: general machine learning (i.e., none of the above)
Submission Number: 4562