FAFTransformer: Multivariate time series prediction method based on multi‐period feature recombination
Abstract Multivariate time series forecasting is widely used in various fields in real life. Many time series prediction models have been proposed. The current forecasting model lacks the mining of correlation between sequences based on different periods and correlation of periodical features betwee...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-10-01
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| Series: | Electronics Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ell2.70069 |
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| _version_ | 1850265950610259968 |
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| author | WenChang Zhang LuZhi Yuan Yun Sha LingLin Yang XueJun Liu Yong Yan |
| author_facet | WenChang Zhang LuZhi Yuan Yun Sha LingLin Yang XueJun Liu Yong Yan |
| author_sort | WenChang Zhang |
| collection | DOAJ |
| description | Abstract Multivariate time series forecasting is widely used in various fields in real life. Many time series prediction models have been proposed. The current forecasting model lacks the mining of correlation between sequences based on different periods and correlation of periodical features between different periods when dealing with data. In this paper, we propose a multivariate data prediction model FAFTransformer based on the reorganization of multi‐periodic features, which first extracts the multi‐periodic information of the time series using the method of frequency domain analysis. The temporal dependencies within sequences are then captured using convolution based on different periods, and the correlations between sequences are learned by combining the multivariate attention mechanism to obtain the intra‐sequence and inter‐sequence correlations under the same period. Finally, period fusion is proposed to capture the correlation of period characteristics between different periods. The experimental results show that the model achieves the best results on multiple datasets compared to the latest seven predictive models for time‐series data. |
| format | Article |
| id | doaj-art-b726912edbcf43fbacc677d5544f38f3 |
| institution | OA Journals |
| issn | 0013-5194 1350-911X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj-art-b726912edbcf43fbacc677d5544f38f32025-08-20T01:54:16ZengWileyElectronics Letters0013-51941350-911X2024-10-016020n/an/a10.1049/ell2.70069FAFTransformer: Multivariate time series prediction method based on multi‐period feature recombinationWenChang Zhang0LuZhi Yuan1Yun Sha2LingLin Yang3XueJun Liu4Yong Yan5Information Engineering Institute Beijing Institute of Petrochemical Technology Beijing ChinaInformation Engineering Institute Beijing Institute of Petrochemical Technology Beijing ChinaInformation Engineering Institute Beijing Institute of Petrochemical Technology Beijing ChinaInformation Engineering Institute Beijing Institute of Petrochemical Technology Beijing ChinaInformation Engineering Institute Beijing Institute of Petrochemical Technology Beijing ChinaInformation Engineering Institute Beijing Institute of Petrochemical Technology Beijing ChinaAbstract Multivariate time series forecasting is widely used in various fields in real life. Many time series prediction models have been proposed. The current forecasting model lacks the mining of correlation between sequences based on different periods and correlation of periodical features between different periods when dealing with data. In this paper, we propose a multivariate data prediction model FAFTransformer based on the reorganization of multi‐periodic features, which first extracts the multi‐periodic information of the time series using the method of frequency domain analysis. The temporal dependencies within sequences are then captured using convolution based on different periods, and the correlations between sequences are learned by combining the multivariate attention mechanism to obtain the intra‐sequence and inter‐sequence correlations under the same period. Finally, period fusion is proposed to capture the correlation of period characteristics between different periods. The experimental results show that the model achieves the best results on multiple datasets compared to the latest seven predictive models for time‐series data.https://doi.org/10.1049/ell2.70069artificial intelligencedata analysisneural net architecturePeriodic fusiontime series prediction |
| spellingShingle | WenChang Zhang LuZhi Yuan Yun Sha LingLin Yang XueJun Liu Yong Yan FAFTransformer: Multivariate time series prediction method based on multi‐period feature recombination Electronics Letters artificial intelligence data analysis neural net architecture Periodic fusion time series prediction |
| title | FAFTransformer: Multivariate time series prediction method based on multi‐period feature recombination |
| title_full | FAFTransformer: Multivariate time series prediction method based on multi‐period feature recombination |
| title_fullStr | FAFTransformer: Multivariate time series prediction method based on multi‐period feature recombination |
| title_full_unstemmed | FAFTransformer: Multivariate time series prediction method based on multi‐period feature recombination |
| title_short | FAFTransformer: Multivariate time series prediction method based on multi‐period feature recombination |
| title_sort | faftransformer multivariate time series prediction method based on multi period feature recombination |
| topic | artificial intelligence data analysis neural net architecture Periodic fusion time series prediction |
| url | https://doi.org/10.1049/ell2.70069 |
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