# 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
```

 -->