ChunjingXiao / DiffAD

Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models, KDD 2023

Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models

This is an repository hosting the code of our paper: Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models, KDD 2023. https://dl.acm.org/doi/abs/10.1145/3580305.3599391

Citation

@inproceedings{xiao2023imputation,
      title={Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models},
      author={Xiao, Chunjing and Gou, Zehua and Tai, Wenxin and Zhang, Kunpeng and Zhou, Fan},
      booktitle={Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
      pages={2742--2751},
      year={2023}
}

Datasets

1. PSM (PooledServer Metrics) is collected internally from multiple application server nodes at eBay. You can learn about it from Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization .
2. MSL (Mars Science Laboratory rover) is a public dataset from NASA. You can learn about it from Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding.
3. SMAP (Soil Moisture Active Passive satellite) also is a public dataset from NASA. You can learn about it from Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding.
4. SMD (Server Machine Dataset) is a 5-week-long dataset collected from a large Internet company. You can learn about it from Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network .
5. SWaT (Secure Water Treatment) is obtained from 51 sensors of the critical infrastructure system under continuous operations. You can learn about it from SWaT: a water treatment testbed for research and training on ICS security .

Usage

Environment

Install Python 3.8.

pip install -r requirements.txt

By default, datasets are placed under the "tf_dataset" folder. If you need to change the dataset, you can modify the dataset path in the json file in the "config" folder. Here is an example of modifying the training dataset path:

"datasets": {
    "train|test": {
        "dataroot": "tf_dataset/smap/smap_train.csv",
        //"dataroot": "tf_dataset/swat/swat_train.csv"
    }
},

In addition, we provide json configuration files for two datasets (SMAP and PSM) for reference.

Training

Next, we demonstrate using the SMAP dataset.

We use dataset SMAP for training demonstration.

# Use time_train.py to train the task.
# Edit json files to adjust dataset path, network structure and hyperparameters.
python time_train.py -c config/smap_time_train.json

Test

The trained model is placed in "experiments/*/checkpoint/" by default. If you need to modify this path, you can refer to "config/smap_time_test.json":

"path": {
  "resume_state": "experiments/SMAP_TRAIN_128_2048_100/checkpoint/E100"
},

We also use dataset SMAP for testing demonstration.

# Edit json to adjust pretrain model path and dataset_path.
python time_test.py -c config/smap_time_test.json

RESULT

The GPU we use is NVIDIA RTX3090 24GB, the training time is about 1 hour, and the test time is about half an hour. The following is the F1-score obtained after testing the SMAP dataset.