Variable Length Motif Discovery in Time Series Data

Abstract

The detection of recurring behavioral patterns in time series data, also called motif discovery, is a crucial step for mining insights in complex time series data, especially in complex environments where manual monitoring is not feasible. However, current state-of-the-art algorithms fall short in their applicability in production environments (due to static motif length, lots of user defined parameters, only providing the best motif pair, etc.). In this paper, a variable length motif discovery method is proposed based on the Matrix Profile which focuses on industrial applicability. It works in noisy and periodic environments, returns only unique motifs (meaning same shape motifs are grouped together as one) and only requires one distance matrix calculation. The method was benchmarked on synthetic data as well as publicly available real world key performance indicator (KPI) data from telecom providers and shows adequate accuracy in finding both short and long motifs in the same time series.