Summary of Pate: Proximity-aware Time Series Anomaly Evaluation, by Ramin Ghorbani et al.
PATE: Proximity-Aware Time series anomaly Evaluation
by Ramin Ghorbani, Marcel J.T. Reinders, David M.J. Tax
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce a new evaluation metric called Proximity-Aware Time series anomaly Evaluation (PATE) to assess the performance of anomaly detection algorithms in time series data. Traditional metrics are inadequate because they assume independent and identically distributed (iid) data, whereas real-world anomalies exhibit complex temporal dynamics and characteristics such as early or delayed detections. PATE incorporates the temporal relationship between prediction and anomaly intervals, using proximity-based weighting with buffer zones around anomaly intervals to provide a more detailed assessment of detection performance. The metric computes a weighted version of the area under the Precision-Recall curve, outperforming other evaluation metrics in experiments with synthetic and real-world datasets. State-of-the-art anomaly detectors are tested across various benchmark datasets using PATE, revealing that a common metric like Point-Adjusted F1 Score is insufficient for characterizing detection performances, whereas PATE provides a more fair model comparison. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly detection in time series data is important because it can help make better decisions in fields like finance and healthcare. However, traditional methods don’t work well because they assume the data is random, but real-world anomalies have patterns that need to be taken into account. The researchers introduce a new way to evaluate how well an algorithm detects anomalies, called PATE (Proximity-Aware Time series anomaly Evaluation). This method takes into account the timing of when an anomaly was detected and how close it was to the actual time the anomaly occurred. They test their approach with different algorithms and real-world datasets and show that it’s better at evaluating performance than other methods. |
Keywords
» Artificial intelligence » Anomaly detection » F1 score » Precision » Recall » Time series
