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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2401.03001 |
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| _version_ | 1866914825136766976 |
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| author | Chu, Li Bingjia, Xiao Qiping, Yuan |
| author_facet | Chu, Li Bingjia, Xiao Qiping, Yuan |
| contents | Recently, Transformer-base models have made significant progress in the field of time series prediction which have achieved good results and become baseline models beyond Dlinear. The paper proposes an U-Net time series prediction model (UnetTSF) with linear complexity, which adopts the U-Net architecture. We are the first to use FPN technology to extract features from time series data, replacing the method of decomposing time series data into trend and seasonal terms, while designing a fusion structure suitable for time series data. After testing on 8 open-source datasets, compared to the best linear model DLiner. Out of 32 testing projects, 31 achieved the best results. The average decrease in mse is 10.1%, while the average decrease in mae is 9.1%. Compared with the complex transformer-base PatchTST, UnetTSF obtained 9 optimal results for mse and 15 optimal results for mae in 32 testing projects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03001 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UnetTSF: A Better Performance Linear Complexity Time Series Prediction Model Chu, Li Bingjia, Xiao Qiping, Yuan Machine Learning Recently, Transformer-base models have made significant progress in the field of time series prediction which have achieved good results and become baseline models beyond Dlinear. The paper proposes an U-Net time series prediction model (UnetTSF) with linear complexity, which adopts the U-Net architecture. We are the first to use FPN technology to extract features from time series data, replacing the method of decomposing time series data into trend and seasonal terms, while designing a fusion structure suitable for time series data. After testing on 8 open-source datasets, compared to the best linear model DLiner. Out of 32 testing projects, 31 achieved the best results. The average decrease in mse is 10.1%, while the average decrease in mae is 9.1%. Compared with the complex transformer-base PatchTST, UnetTSF obtained 9 optimal results for mse and 15 optimal results for mae in 32 testing projects. |
| title | UnetTSF: A Better Performance Linear Complexity Time Series Prediction Model |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.03001 |