DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

This repository contains an implementation of the paper "DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series." The original paper can be found here.

About the Paper

The paper, authored by Mohsin Munir, Shoaib Ahmed Siddiqui, Andreas Dengel, and Sheraz Ahmed, presents DeepAnT, a novel deep learning model designed for unsupervised anomaly detection in time series data.

• Model Structure: The model consists of a sequence of convolutional layers followed by fully connected layers to capture local patterns and long-term dependencies. Below is the model structure from the paper:
• Unsupervised Learning: Suitable for applications with scarce labeled anomalies.
• Anomaly Detection: Detects anomalies by comparing predicted values with actual values and calculating anomaly scores.

Implementation Details

This implementation follows the architecture and methodology described in the paper using PyTorch and PyTorch Lightning.

• Sliding Window: Preprocesses the time series data using a sliding window approach.
• Forecasting-Based Model: Predicts the next value(s) in the sequence for anomaly detection.
• Training and Validation:
  • Initial training without considering the validation loss.
  • After the initial training, the best model is selected based on the validation loss, using early stopping to prevent overfitting.
• Dynamic Threshold Calculation: Threshold for anomaly detection procedure is dynamically calculated based on the anomaly scores' statistics.
• Visualization: Provides visualizations for predicted sequences as well as detected anomalies.

Results

The model was trained and validated on the 1D NAB dataset. Below are key results from the training run:

Training and Validation

• Final Training Loss: 0.0024
• Final Validation Loss: 0.0032

Anomaly Detection

• Dynamic Threshold: 0.0276
• Detected Anomalies: Anomalies detected at indices [54, 55, 84, 132, 134, 135, 139, 141, 142, 144]

Visualizations

The results of the model's predictions and anomaly detection are visualized as follows:

Usage

1. Clone the repository:

   git clone https://github.com/EnsiyeTahaei/DeepAnT.git
   cd DeepAnT

2. Install the required packages:

   pip install -r requirements.txt

3. Run the main script:

   python main.py --dataset_name <dataset_name>

Note: dataset_name is optional. If not provided, it defaults to "NAB".

License

This project is licensed under the MIT License.

Citation

If you use this code for your research, please cite the original paper:

@ARTICLE{8581424, author={Munir, Mohsin and Siddiqui, Shoaib Ahmed and Dengel, Andreas and Ahmed, Sheraz}, journal={IEEE Access}, title={DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series}, year={2019}, volume={7}, number={}, pages={1991-2005}, keywords={Anomaly detection;Time series analysis;Clustering algorithms;Data models;Benchmark testing;Heuristic algorithms;Anomaly detection;artificial intelligence;convolutional neural network;deep neural networks;recurrent neural networks;time series analysis}, doi={10.1109/ACCESS.2018.2886457} }