Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria

Abstract

The automated detection of points in a time series with a special meaning to a user, commonly referred to as the detection of events, is an important aspect of temporal data mining. These events often are points in a time series that can be peaks, level changes, sudden changes of spectral characteristics, etc. Fast algorithms are needed for event detection for online applications or applications with huge time series data sets. In this article, we present a very fast algorithm for event detection that learns detection criteria from labeled sample time series (i.e., time series where events are marked). This algorithm is based on fast transformations of time series into low-dimensional feature spaces and probabilistic modeling techniques to identify criteria in a supervised manner. Events are then found in one, single fast pass over the signal (therefore, the algorithm is called SwiftEvent) by evaluating learned thresholds on Mahalanobis distances in the feature space. We analyze the run-time complexity of SwiftEvent and demonstrate its application in some use cases with artificial and real-world data sets in comparison with other state-of-the-art techniques.