Mining Recurring Patterns in Real-Valued Time Series using the Radius Profile

Abstract

Time series analysis is becoming more popular in both research and industry. One recent innovation is the Ostinato algorithm, which finds the best preserved patterns that are repeated in a collection of series, i.e. consensus motifs and corresponding radii. However, Ostinato only works as a batch algorithm, can only find the top-k patterns, only finds patterns that are repeated in multiple series and has a runtime that depends on the input series and setup parameters. To tackle these limitations, we present two algorithms in this paper that can answer broader questions. First, we created an anytime version of Ostinato, called Anytime Ostinato, that finds the exact consensus radius for each subsequence, i.e. the radius profile, or can estimate these radii in a fraction of the time. Second, we designed a batch algorithm, called Single Series Ostinato, that finds the radius profile for a single series allowing us to detect repeating patterns in a single series, which is not possible for Ostinato. In this paper we explain both algorithms and apply them to the REFIT and PAMAP2 datasets respectively.