TY - DATA T1 - Code underlying the publication: "PATE: Proximity-Aware Time series anomaly Evaluation" PY - 2024/12/20 AU - Ramin Ghorbani AU - Marcel Reinders AU - David Tax UR - DO - 10.4121/d8b0f4ab-3bd6-412b-88dd-f515d545aefd.v1 KW - Time Series KW - Anomaly Detection KW - Evaluation Metrics KW - Precision KW - Recall N2 -

This repository provides the implementation of Proximity-Aware Time Series Anomaly Evaluation (PATE), a novel metric introduced to address the limitations of existing evaluation methods for time series anomaly detection. PATE incorporates proximity-based weighting with buffer zones around anomaly intervals to account for temporal complexities such as Early or Delayed detections, Onset response time, and Coverage level. It computes a weighted version of the Area Under Precision and Recall curve, offering a more accurate and meaningful assessment of anomaly detection models. Experimental results validate PATE's ability to distinguish performance differences across various models and scenarios.

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