Assessment and Adaption of Pattern Discovery Approaches for Time Series Under the Requirement of Time Warping

Abstract

In the automotive industry, the cars themselves as well as the production lines, produce a high amount of data day by day. To get information out of these data there is a need for high performance data mining tools. One of these tools is the pattern discovery. This paper addresses the assessment of different approaches to discover frequent pattern in time series. Our special requirement is the detection of time warped pattern with variable length. The comparison includes approaches based on dynamic time warping (DTW), discretization as well as Keogh’s Matrix Profile. Every approach is exemplarily implemented in MATLAB and (if necessary) adapted to face our use cases. The focus of the assessment will be the quality of the results, the runtime and the effort of parametrization. For evaluation, time series test datasets are generated with predefined patterns based on random walks. The output patterns, identified by the different pattern discovery algorithms, are compared with the initial patterns and evaluated with respect to the Jaccard index. This leads to a quality score for every algorithm and every parametrization and the possibility to compare different algorithms as well as approaches.