Metadata-Version: 2.1
Name: tsadmetrics
Version: 0.1.17
Summary: Librería para evaluación de detección de anomalías en series temporales
Home-page: https://github.com/pathsko/TSADmetrics
Author: Pedro Rafael Velasco Priego
Author-email: Pedro Rafael Velasco Priego <i12veprp@uco.es>
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: joblib==1.4.2
Requires-Dist: numpy==1.24.4
Requires-Dist: pandas==2.0.3
Requires-Dist: PATE==0.1.1
Requires-Dist: patsy==0.5.6
Requires-Dist: python-dateutil==2.9.0.post0
Requires-Dist: pytz==2024.1
Requires-Dist: scikit-learn==1.3.2
Requires-Dist: scipy==1.10.1
Requires-Dist: six==1.16.0
Requires-Dist: statsmodels==0.14.1
Requires-Dist: threadpoolctl==3.5.0
Requires-Dist: tzdata==2024.1

# 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
