SoftED: Metrics for Soft Evaluation of Time Series Event Detection
This addresses the need for better evaluation metrics in time series event detection, particularly for applications where neighboring detections are valuable, though it is incremental as it builds on existing metric concepts.
The paper tackled the problem of evaluating time series event detection methods, which rely on standard classification metrics that ignore temporal tolerance for neighboring detections, and introduced SoftED metrics that improved evaluation by incorporating temporal tolerance in over 36% of experiments.
Time series event detection methods are evaluated mainly by standard classification metrics that focus solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighboring detections. These detections are valuable to trigger necessary actions or help mitigate unwelcome consequences. In this context, current metrics are insufficient and inadequate for the context of event detection. There is a demand for metrics that incorporate both the concept of time and temporal tolerance for neighboring detections. This paper introduces SoftED metrics, a new set of metrics designed for soft evaluating event detection methods. They enable the evaluation of both detection accuracy and the degree to which their detections represent events. They improved event detection evaluation by associating events and their representative detections, incorporating temporal tolerance in over 36\% of experiments compared to the usual classification metrics. SoftED metrics were validated by domain specialists that indicated their contribution to detection evaluation and method selection.