Learning in Two-Scales Through LSTM-GPT2 Fusion Network: A Hybrid Approach for Time Series Anomaly Detection
- DOI
- 10.3390/s25061849
- Published
- 2025-03-16
- Container
- Sensors
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/s25061849,
title = {Learning in Two-Scales Through LSTM-GPT2 Fusion Network: A Hybrid Approach for Time Series Anomaly Detection},
author = {Taoyu Wang and Dan Wu and Jun Wang and Jinwei Zhao and Haoming Wang and Dongnan Xie and Hongtao Zhang and Xinhong Hei},
year = {2025},
journal = {Sensors},
doi = {10.3390/s25061849},
url = {https://doi.org/10.3390/s25061849}
}RIS
TY - JOUR TI - Learning in Two-Scales Through LSTM-GPT2 Fusion Network: A Hybrid Approach for Time Series Anomaly Detection AU - Taoyu Wang AU - Dan Wu AU - Jun Wang AU - Jinwei Zhao AU - Haoming Wang AU - Dongnan Xie AU - Hongtao Zhang AU - Xinhong Hei PY - 2025 JO - Sensors DO - 10.3390/s25061849 UR - https://doi.org/10.3390/s25061849 ER -
APA
Wang, T., Wu, D., Wang, J., Zhao, J., Wang, H., Xie, D., Zhang, H., & Hei, X. (2025). Learning in Two-Scales Through LSTM-GPT2 Fusion Network: A Hybrid Approach for Time Series Anomaly Detection. Sensors. https://doi.org/10.3390/s25061849
Source records
- crossref · retrieved 2026-11-06T17:13:44.314Z