  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.
