The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/d8b0f4ab-3bd6-412b-88dd-f515d545aefd

Ghorbani, Ramin; Reinders, Marcel; Tax, David (2024): Code underlying the publication: "PATE: Proximity-Aware Time series anomaly Evaluation". Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/d8b0f4ab-3bd6-412b-88dd-f515d545aefd.v1

Software

• Artificial Intelligence and Image Processing
• Information and Computing Sciences

Anomaly Detection, Evaluation Metrics, Precision, Recall, Time Series

MIT

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Ramin Ghorbani , Marcel Reinders , David Tax

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.

• 2024-12-20 first online, published, posted

4TU.ResearchData

Script/.py Configuration/.json Documentation/.md Dependency/.txt License/LICENSE Version Control/.gitignore, .gitattributes

PATE: Proximity-Aware Time series anomaly Evaluation

• https://doi.org/10.1145/3637528.3671971

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Pattern Recognition & Bioinformatics Group