PERUSE: An unsupervised algorithm for finding recurring patterns in time series

This paper describes PERUSE, an unsupervised algorithm for finding recurring patterns in time series. It was initially developed and tested with sensor data from a mobile robot, i.e. noisy, real-valued, multivariate time series with variable intervals between observations. The pattern discovery problem is decomposed into two subproblems: (1) a supervised learning problem in which a teacher provides exemplars of patterns and labels time series according to whether they contain the patterns; (2) an unsupervised learning problem in which the time series are used to generate an approximation to the teacher. Experimental results show that PERUSE can discover patterns in audio data corresponding to recurring words in natural language utterances and patterns in the sensor data of a mobile robot corresponding to qualitatively distinct outcomes of taking actions.