https://github.com/zhangzw16/tadnet

Code for ICASSP 2024 paper "Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection".
https://github.com/zhangzw16/tadnet

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Code for ICASSP 2024 paper "Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection".

• Host: GitHub
• URL: https://github.com/zhangzw16/tadnet
• Owner: zhangzw16
• License: mit
• Created: 2023-12-14T05:32:22.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-04-13T13:51:59.000Z (9 months ago)
• Last Synced: 2024-04-14T04:10:37.674Z (9 months ago)
• Language: Python
• Size: 44.9 KB
• Stars: 4
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
> [!IMPORTANT] 

> We are still working on the readme, stay tunned...

# [TADNet] Unravel Anomalies: An End-to-End Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection

This repository This repository contains the code for the paper "[Unravel Anomalies: An End-to-End Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection](https://ieeexplore.ieee.org/document/10446482)" by *Zhenwei Zhang; Ruiqi Wang; Ran Ding; Yuantao Gu*, published in the IEEE ICASSP 2024 (International Conference on Acoustics, Speech, and Signal Processing).

## Introduction

:triangular_flag_on_post: Presentation Slides for this paper can be found on [IEEE SigPort](https://sigport.org/documents/unravel-anomalies-end-end-seasonal-trend-decomposition-approach-time-series-anomaly) ([Download](https://sigport.org/sites/default/files/docs/TADNet%20Oral.pdf)). 

> Traditional Time-series Anomaly Detection (TAD) methods often struggle with the composite nature of complex time-series data and a diverse array of anomalies. We introduce TADNet, an end-to-end TAD model that leverages Seasonal-Trend Decomposition to link various types of anomalies to specific decomposition components, thereby simplifying the analysis of complex time-series and enhancing detection performance. Our training methodology, which includes pre-training on a synthetic dataset followed by fine-tuning, strikes a balance between effective decomposition and precise anomaly detection. Experimental validation on real-world datasets confirms TADNet’s state-of-the-art performance across a diverse range of anomalies.

## Datasets

For more details on the datasets used in the paper, please refer to [this repo](https://github.com/imperial-qore/TranAD/tree/main/data).

- UCR:

- SMD:

- SWaT:

- PSM:

- WADI:

## Preparation

Generate the synthetic dataset using the command:

```bash

python run.py --mode synthetic 

```

## Training & Evaluation

Train the model using the command:

```bash

python run.py --mode pretrain --loss 2

python run.py --file_dir xxx.npy --mode finetune --loss 5 --number xxx --exists 1

```

Evaluate the model using the command:

```bash

python run.py --file_dir xxx.npy --mode test --number xxx --exists 1

```

## Citation

If you find this work useful, please consider citing the following paper:

```bibtex

@INPROCEEDINGS{10446482,

  author={Zhang, Zhenwei and Wang, Ruiqi and Ding, Ran and Gu, Yuantao},

  booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 

  title={Unravel Anomalies: an End-to-End Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection}, 

  year={2024},

  volume={},

  number={},

  pages={5415-5419},

  keywords={Training;Analytical models;Time series analysis;Data visualization;Signal processing;Data models;Arrays;time-series anomaly detection;seasonal-trend decomposition;time-series analysis;end-to-end},

  doi={10.1109/ICASSP48485.2024.10446482}}

```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.