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Recently, Transformer-base models have made significant progress in the fieldof time series prediction which have achieved good results and become baselinemodels beyond Dlinear. The paper proposes an U-Net time series prediction model(UnetTSF) with linear complexity, which adopts the U-Net architecture. We arethe first to use FPN technology to extract features from time series data,replacing the method of decomposing time series data into trend and seasonalterms, while designing a fusion structure suitable for time series data. Aftertesting on 8 open-source datasets, compared to the best linear model DLiner.Out of 32 testing projects, 31 achieved the best results. The average decreasein mse is 10.1%, while the average decrease in mae is 9.1%. Compared with thecomplex transformer-base PatchTST, UnetTSF obtained 9 optimal results for mseand 15 optimal results for mae in 32 testing projects.

UnetTSF: A Better Performance Linear Complexity Time Series Prediction Model