tsadmetrics 1.0.13

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.

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Key features

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

  • SPM Metrics: Evaluate predictions at each point independently, ignoring temporal continuity.
  • TEM Metrics: Consider temporal context, analyzing when and how anomalies occur.
    • TPDM: Partial detection within a real anomaly event counts as correct.
    • PTDM: Requires detection to cover a significant fraction of the real anomaly.
    • TMEM: Measures alignment of real vs predicted anomaly events.
    • DPM: Penalizes late detections.
    • TSTM: 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.

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Getting the library

The project can be download in this repository. Detailed information about getting and running the library can be consulted in user’s manual where all dependencies and libraries necessaries are commented.

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Installation

Install TSADmetrics via pip:

pip install tsadmetrics

Tutorials and documentation

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

User's manual can be found in the main directory documentation of this repository and includes:

• Getting and installing the library.
• A description of library architecture.
• Run metrics in the library.
• Add new metrics in the library.
• API reference.

Metric included

TSADmetrics includes 34 metrics, organized according to the proposed taxonomy for time series anomaly detection metrics.

Category	Subcategory	Metric
SPM	—	PointwiseFScore
SPM	—	DiceCoefficient
SPM	—	PointwiseAucRoc
SPM	—	PointwiseAucPr
SPM	—	PrecisionAtK
TEM	TPDM	PointadjustedFScore
TEM	TPDM	BalancedPointadjustedFScore
TEM	TPDM	SegmentwiseFScore
TEM	TPDM	CompositeFScore
TEM	TPDM	PointadjustedAucPr
TEM	TPDM	PointadjustedAucRoc
TEM	TPDM	RangebasedFScore
TEM	DPM	DelayThresholdedPointadjustedFScore
TEM	DPM	EarlyDetectionScore
TEM	DPM	LatencySparsityawareFScore
TEM	DPM	MeanTimeToDetect
TEM	DPM	NabScore
TEM	PTDM	AverageDetectionCount
TEM	PTDM	TotalDetectedInRange
TEM	PTDM	DetectionAccuracyInRange
TEM	PTDM	WeightedDetectionDifference
TEM	PTDM	PointadjustedAtKFScore
TEM	PTDM	PointadjustedAtKLFScore
TEM	PTDM	TimeseriesAwareFScore
TEM	TMEM	AbsoluteDetectionDistance
TEM	TMEM	EnhancedTimeseriesAwareFScore
TEM	TMEM	TemporalDistance
TEM	TSTM	AffiliationbasedFScore
TEM	TSTM	NormalizedAffiliationbasedFScore
TEM	TSTM	PateFScore
TEM	TSTM	Pate
TEM	TSTM	TimeTolerantFScore
TEM	TSTM	VusRoc
TEM	TSTM	VusPr

Acknowledgements

This library was supported in part by the PID2023-148396NB-I00 project of Spanish Ministry of Science and Innovation and the European Regional Development Fund, by the ProyExcel-0069 project of the Andalusian University, Research and Innovation Department.

Citation

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References

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

Licence

The tools is free and open source, under the GNU General Public GPLv3 license.

Reporting bugs

Feel free to open an issue at Github if anything is not working as expected. Merge request are also encouraged, it will be carefully reviewed and merged if everything is all right.