U-Net Inspired Transformer Architecture for Multivariate Time Series Synthesis
This study introduces a Multiscale Dual-Attention U-Net (TS-MSDA U-Net) model for long-term time series synthesis. By integrating multiscale temporal feature extraction and dual-attention mechanisms into the U-Net backbone, the model captures complex temporal dependencies more effectively. The model...
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/4073 |
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| author | Shyr-Long Jeng |
| author_facet | Shyr-Long Jeng |
| author_sort | Shyr-Long Jeng |
| collection | DOAJ |
| description | This study introduces a Multiscale Dual-Attention U-Net (TS-MSDA U-Net) model for long-term time series synthesis. By integrating multiscale temporal feature extraction and dual-attention mechanisms into the U-Net backbone, the model captures complex temporal dependencies more effectively. The model was evaluated in two distinct applications. In the first, using multivariate datasets from 70 real-world electric vehicle (EV) trips, TS-MSDA U-Net achieved a mean absolute error below 1% across key parameters, including battery state of charge, voltage, acceleration, and torque—representing a two-fold improvement over the baseline TS-p2pGAN. While dual-attention modules provided only modest gains over the basic U-Net, the multiscale design enhanced overall performance. In the second application, the model was used to reconstruct high-resolution signals from low-speed analog-to-digital converter data in a prototype resonant CLLC half-bridge converter. TS-MSDA U-Net successfully learned nonlinear mappings and improved signal resolution by a factor of 36, outperforming the basic U-Net, which failed to recover essential waveform details. These results underscore the effectiveness of transformer-inspired U-Net architectures for high-fidelity multivariate time series modeling in both EV analytics and power electronics. |
| format | Article |
| id | doaj-art-85a8cc9f1d344c7ea168fce8e23293f1 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-85a8cc9f1d344c7ea168fce8e23293f12025-08-20T03:17:51ZengMDPI AGSensors1424-82202025-06-012513407310.3390/s25134073U-Net Inspired Transformer Architecture for Multivariate Time Series SynthesisShyr-Long Jeng0Department of Mechanical Engineering, Lunghwa University of Science and Technology, Taoyuan City 333326, TaiwanThis study introduces a Multiscale Dual-Attention U-Net (TS-MSDA U-Net) model for long-term time series synthesis. By integrating multiscale temporal feature extraction and dual-attention mechanisms into the U-Net backbone, the model captures complex temporal dependencies more effectively. The model was evaluated in two distinct applications. In the first, using multivariate datasets from 70 real-world electric vehicle (EV) trips, TS-MSDA U-Net achieved a mean absolute error below 1% across key parameters, including battery state of charge, voltage, acceleration, and torque—representing a two-fold improvement over the baseline TS-p2pGAN. While dual-attention modules provided only modest gains over the basic U-Net, the multiscale design enhanced overall performance. In the second application, the model was used to reconstruct high-resolution signals from low-speed analog-to-digital converter data in a prototype resonant CLLC half-bridge converter. TS-MSDA U-Net successfully learned nonlinear mappings and improved signal resolution by a factor of 36, outperforming the basic U-Net, which failed to recover essential waveform details. These results underscore the effectiveness of transformer-inspired U-Net architectures for high-fidelity multivariate time series modeling in both EV analytics and power electronics.https://www.mdpi.com/1424-8220/25/13/4073attentionCLLC converterhalf-bridgetime series synthesiselectric vehicle |
| spellingShingle | Shyr-Long Jeng U-Net Inspired Transformer Architecture for Multivariate Time Series Synthesis Sensors attention CLLC converter half-bridge time series synthesis electric vehicle |
| title | U-Net Inspired Transformer Architecture for Multivariate Time Series Synthesis |
| title_full | U-Net Inspired Transformer Architecture for Multivariate Time Series Synthesis |
| title_fullStr | U-Net Inspired Transformer Architecture for Multivariate Time Series Synthesis |
| title_full_unstemmed | U-Net Inspired Transformer Architecture for Multivariate Time Series Synthesis |
| title_short | U-Net Inspired Transformer Architecture for Multivariate Time Series Synthesis |
| title_sort | u net inspired transformer architecture for multivariate time series synthesis |
| topic | attention CLLC converter half-bridge time series synthesis electric vehicle |
| url | https://www.mdpi.com/1424-8220/25/13/4073 |
| work_keys_str_mv | AT shyrlongjeng unetinspiredtransformerarchitectureformultivariatetimeseriessynthesis |