# TSADmetrics - Time Series Anomaly Detection Metrics

**TSADmetrics** is a Python library for evaluating anomaly detection algorithms in time series data. It provides a comprehensive set of binary and non-binary metrics designed specifically for the challenges of anomaly detection in temporal contexts.

## Features

- **Binary Metrics**: Evaluate discrete anomaly predictions (0/1 labels)

- **Non-Binary Metrics**: Assess continuous anomaly scores

- **Efficient Computation**: Compute multiple metrics at once

- **CLI Tool**: Evaluate metrics directly from CSV/JSON files

## Installation

Install TSADmetrics via pip:

```bash
pip install tsadmetrics
```

## Documentation

The complete documentation for TSADmetrics is available at:  
📚 [https://tsadmetrics.readthedocs.io/](https://tsadmetrics.readthedocs.io/)

## Acknowledgements

This library is based on the concepts and implementations from:  
Sørbø, S., & Ruocco, M. (2023). *Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series*. https://doi.org/10.1007/s10618-023-00988-8