Behave or Be Detected! Identifying Outlier Sequences by Their Group Cohesion

Since the amount of sequentially recorded data is constantly increasing, the analysis of time series (TS), and especially the identification of anomalous points and subsequences, is nowadays an important field of research. Many approaches consider only a single TS, but in some cases multiple sequences need to be investigated. In 2019 we presented a new method to detect behavior-based outliers in TS which analyses relations of sequences to their peers. Therefore we clustered data points of TS per timestamp and calculated distances between the resulting clusters of different points in time. We realized this by evaluating the number of peers a TS is moving with. We defined a stability measure for time series and subsequences, which is used to detect the outliers. Originally we considered cluster splits but did not take merges into account. In this work we present two major modifications to our previous work, namely the introduction of the jaccard index as a distance measure for clusters and a weighting function, which enables behavior-based outlier detection in larger TS. We evaluate our modifications separately and in conjunction on two real and one artificial data set. The adjustments lead to well reasoned and sound results, which are robust regarding larger TS.