tsadmetrics 1.0.7

Librería para evaluación de detección de anomalías en series temporales

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 metrics specifically designed to handle the temporal nature of anomalies.

---

Features

• Metric Taxonomy: Metrics are categorized into types based on how they handle temporal context:

  • MPI Metrics: Evaluate predictions at each point independently, ignoring temporal continuity.
  • MET Metrics: Consider temporal context, analyzing when and how anomalies occur.
    • MDPT: Partial detection within a real anomaly event counts as correct.
    • MDTP: Requires detection to cover a significant fraction of the real anomaly.
    • MECT: Measures alignment of real vs predicted anomaly events.
    • MPR: Penalizes late detections.
    • MTDT: Allows temporal tolerance for early or late detections.

• Direct Metric Usage: Instantiate any metric class and call compute() for individual evaluation.

• Batch Evaluation: Use Runner to evaluate multiple datasets and metrics at once, with support for both direct data and CSV/JSON input.

• Flexible Configuration: Load metrics from YAML configuration files or global evaluation config files.

• CLI Tool: Compute metrics directly from files without writing Python code.

---

Installation

Install TSADmetrics via pip:

pip install tsadmetrics

Documentation

The complete documentation for TSADmetrics is available at:
📚 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