| # Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy | |
| This repository contains the implementation of Time-RCD for time series anomaly detection, integrated with the TSB-AD (Time Series Benchmark for Anomaly Detection) datasets. | |
| ## Project Structure | |
| ``` | |
| . | |
| ├── checkpoints/ # Pre-trained model checkpoints | |
| ├── datasets/ # TSB-AD datasets (univariate and multivariate) | |
| ├── evaluation/ # Evaluation metrics and visualization tools | |
| ├── models/ # Model implementations | |
| │ └── time_rcd/ # Time-RCD model components | |
| ├── utils/ # Utility functions | |
| ├── testing.py # Main entry point | |
| ├── model_wrapper.py # Model wrapper for different algorithms | |
| └── README.md # This file | |
| ``` | |
| ## Prerequisites | |
| - Python 3.10 | |
| - conda (recommended for environment management) | |
| - Git | |
| ## Installation | |
| ### 1. Create and Activate Conda Environment | |
| ```bash | |
| conda create -n Time-RCD python=3.10 | |
| conda activate Time-RCD | |
| ``` | |
| ### 2. Download the Repository | |
| ```bash | |
| wget https://anonymous.4open.science/api/repo/TimeRCD-5BE1/zip -O Time-RCD.zip | |
| unzip Time-RCD.zip -d Time-RCD | |
| ``` | |
| or dowload from the link: https://anonymous.4open.science/r/TimeRCD-5BE1 and unzip | |
| ### 3. Download TSB-AD Datasets | |
| Create the datasets directory and download the TSB-AD-U (univariate) and TSB-AD-M (multivariate) datasets: | |
| ```bash | |
| mkdir -p "datasets" \ | |
| && wget -O "datasets/TSB-AD-U.zip" "https://www.thedatum.org/datasets/TSB-AD-U.zip" \ | |
| && wget -O "datasets/TSB-AD-M.zip" "https://www.thedatum.org/datasets/TSB-AD-M.zip" \ | |
| && cd datasets \ | |
| && unzip TSB-AD-U.zip && rm TSB-AD-U.zip \ | |
| && unzip TSB-AD-M.zip && rm TSB-AD-M.zip \ | |
| && cd .. | |
| ``` | |
| ### 4. Install Python Dependencies | |
| #### Option A: Fast Install (using uv) | |
| ```bash | |
| pip install uv | |
| uv pip install jaxtyping einops pandas numpy scikit-learn transformers torch torchvision statsmodels matplotlib seaborn -U "huggingface_hub[cli]" | |
| ``` | |
| #### Option B: Normal Install | |
| ```bash | |
| pip install jaxtyping einops pandas numpy scikit-learn transformers torch torchvision statsmodels matplotlib seaborn -U "huggingface_hub[cli]" | |
| ``` | |
| ### 5. Download Pre-trained Checkpoints | |
| Download the pre-trained model checkpoints from Hugging Face: | |
| ```bash | |
| huggingface-cli download thu-sail-lab/Time-RCD checkpoints.zip --local-dir ./ | |
| unzip checkpoints.zip | |
| ``` | |
| ### Single Variable Time Series | |
| To run anomaly detection on univariate time series: | |
| ```bash | |
| python testing.py | |
| ``` | |
| ### Multi-Variable Time Series | |
| To run anomaly detection on multivariate time series: | |
| ```bash | |
| python testing.py --mode multi | |
| ``` | |
| <!-- | |
| ### 6. Download Training Datasets | |
| Download Training Datasets (Optional -for retraining models) | |
| ```bash | |
| mkdir training_data | |
| huggingface-cli download thu-sail-lab/Time-RCD training_data.zip --local-dir ./ | |
| unzip training_data.zip | |
| ``` | |
| --> | |