This repository provides a PyTorch implementation of PPLAD (paper).

The recommended requirements for PPLAD are specified as follows:

• torch==1.13.0
• numpy==1.26.4
• pandas==2.2.2
• scikit-learn==1.5.1
• matplotlib==3.9.2
• statsmodels==0.14.2
• tsfresh==0.20.3
• hurst==0.0.5
• arch==7.0.0

The dependencies can be installed by:

pip install -r requirements.txt

The datasets can be obtained and put into datasets/ folder in the following way:

• Our model supports anomaly detection for multivariate time series datasets.
• We provide the SKAB dataset. If you want to use your own dataset, please place your datasetfiles in the /dataset/<dataset>/ folder, following the format <dataset>_train.npy, <dataset>_test.npy, <dataset>_test_label.npy.

There are six files/folders in the source

• data_factory: The preprocessing folder/file. All datasets preprocessing codes are here.

• main.py: The main python file. You can adjustment all parameters in there.

• metrics: There is the evaluation metrics code folder.

• model: PPLAD model folder

• solver.py: Another python file. The training, validation, and testing processing are all in there

• requirements.txt: Python packages needed to run this repo

• Usage

1. Install Python 3.9, PyTorch >= 1.4.0
2. Download the datasets
3. To train and evaluate PPLAD on a dataset, run the following command:

python main.py 

@ARTICLE{10908726,
  author={Chen, Lei and Xu, Yepeng and Li, Ming and Hu, Bowen and Guo, Haomiao and Liu, Zhaohua},
  journal={IEEE Transactions on Industrial Informatics}, 
  title={Privacy-Preserving Lightweight Time-Series Anomaly Detection for Resource-Limited Industrial IoT Edge Devices}, 
  year={2025},
  volume={21},
  number={6},
  pages={4435-4446},
  keywords={Anomaly detection;Image edge detection;Data models;Computational modeling;Gaussian distribution;Data privacy;Adversarial machine learning;Industrial Internet of Things;Cloud computing;Informatics;Data privacy-preserving;edge devices;industrial Internet of Things;lightweight anomaly detection;resource-limited;similarity discrepancy},
  doi={10.1109/TII.2025.3538127}}