Identifying distinctive subsequences in multivariate time series by clustering

Abstract

Most time series comparison algorithms attempt to discover what the members of a set of time series have in common. We investigate a different problem, determining what distinguishes time series in that set from other time series obtained from the same source. In both cases the goal is to identify shared patterns, though in the latter case those patterns must be distinctiveaswell. An efficient incremental algorithm for identifying distinctive subsequences in multivariate, real-valued time series is described and evaluated with data from two very different sources: the response of a set of bandpass filters to human speech and the sensors of a mobile robot.