Research on anomaly detection model for traffic time series data integrating multiple mechanisms
To enhance the anomaly recognition ability of traffic time series data, a hybrid model was constructed. Firstly, the multi-head attention, residuals and probabilistic sparse self-attention were combined to form a global feature recognition (GFR) module, enhancing the ability while reducing computati...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Hebei University of Science and Technology
2025-06-01
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| Series: | Journal of Hebei University of Science and Technology |
| Subjects: | |
| Online Access: | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202503003?st=article_issue |
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