TimesNet의 구조
Bayesian-Optimization을 통해 적절한 컨볼루션 수를 training 동안 최적화하여 공정 모니터링 이상 탐지에 최적화.
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Bayesian-Optimization
- Checkout timesnet_bayesian_optimization.ipynb.
- Follow the instruction of the file
- There's no need to change or modify.
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Detect Anomalies
- Checkout timesnet_tasks.py.
- Required command line arguments
- task: Use "train" for training TimesNet before detection or use "detect" for only detection. If you want to try simulating anomaly detection with test data, use "simulate".
- data: Data for training TimesNet or detection should be within this directory.
- model_name: Model name for saving trained model, or detecting with corresponding model.
python timesnet_tasks.py --task detect --data PSM_simulation --model_name timesnet - The result is as follows.
---Start detecting anomalies--- Anomaly occured at 2024-05-31 00:56:05 Anomaly occured at 2024-05-31 00:56:05 Anomaly occured at 2024-05-31 00:56:06 Anomaly occured at 2024-05-31 00:56:06 Anomaly occured at 2024-05-31 00:56:08 Anomaly occured at 2024-05-31 00:56:08 Anomaly occured at 2024-05-31 00:56:08 Anomaly occured at 2024-05-31 00:56:09 Anomaly occured at 2024-05-31 00:56:09 Anomaly occured at 2024-05-31 00:56:09평가 지표 경량화 전 경량화 후 모델 성능(AUC-score) 0.9976 0.9979 모델 규모(MB) 18.79 0.1 학습 시간(sec) 418.8087 15.0395 추론 시간(sec) 260.2487 14.6195
If you found this code helpful, please consider citing:
Semin Kim and Soohyun Oh, Minje Park, Jiho Lee & Moongi Seock (2024). Efficient Time-Series Data Anomaly Detection
with a Lightweight TimesNet Model . 대한전자공학회 학술대회, 제주.
- Wu, Haixu, et al. "Timesnet: Temporal 2d-variation modeling for general time series analysis." The eleventh international conference on learning representations. 2022.
- Hongzuo Xu, Guansong Pang, Yijie Wang and Yongjun Wang, "Deep Isolation Forest for Anomaly Detection," in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2023.3270293.
- https://github.com/thuml/Time-Series-Library
- https://github.com/xuhongzuo/DeepOD