Causal Disentanglement Learning for Accurate Anomaly Detection in Multivariate Time Series

Code implementation for CDRL4AD(Causally Disentangled Representation Learning for Anomaly Detection).

❗Key Features

1. Causal Discovery:
   • Utilizes an attention mechanism to identify and learn time-lagged causal relationships within multivariate time series data.
   • Constructs a causal graph to represent these causal relationships, enhancing the model’s understanding of how past data influences future outcomes.
2. Causally Disentangled Representation (CDR):
   • Employs a multi-head decoder variational autoencoder (VAE) to create causally disentangled representations.
   • Ensures each latent variable aligns with predefined causal relationships, allowing for sophisticated data representation and better interpretability.
3. Node and Edge Correlation Representation (NECR):
   • Encodes variable correlations at both node and edge levels using a dual-level Graph Attention Network (GAT).
   • Learns graph structures by connecting nodes with similar patterns, improving the model’s ability to detect anomalies through structural insights.
4. Temporal Dependency Representation (TDR):
   • Captures sequential relationships within the data, accounting for dependencies over time.
   • Aggregates features from past observations to model temporal dependencies, enhancing the detection of anomalies with temporal context.
5. Root Cause Analysis:
   • Provides clear insights into the root causes of detected anomalies by analyzing variable contributions.
   • Identifies top-K root cause variables, facilitating efficient diagnosis and problem-solving in real-time scenarios.
6. Robust Performance:
   • Demonstrates high precision, recall, and F1 scores across diverse datasets, showcasing the model’s robustness to different data characteristics and distributions.
   • Includes comprehensive ablation studies to validate the importance of each model component.

🖥️ Getting Started

Install dependencies (virtual env is recommended):

pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt

To train:

python train.py --batch_size <batch size> --dataset <dataset> --epochs <epoch> --window_size <sliding window size> --embed_dim <embedding dimension> --topk <the value of top k>