Discovery of Typical Subsequences of Time Series on Graphical Processor

Abstract

Discovery of typical subsequences in a time series is one of the topical problems of time series mining. In this problem, we are to find a set of subsequences that adequately represents the specified time series. The solution of such a problem makes it possible to summarize and visualize a large time series in a wide range of applications: monitoring of the technical condition of complex machines and mechanisms, intelligent management of life support systems, monitoring of indicators of functional diagnostics of the human body, etc. The recently proposed snippet concept formalizes a typical time series subsequence as follows. A snippet of a time series is a subsequence that many other subsequences of the given series are similar to, with respect to a specialized similarity measure based on the Euclidean distance. Despite the snippets discovery algorithm shows adequate results for time series from a wide range of subject domains, it has a high computational complexity. In this article, we propose a novel parallel algorithm for snippets discovery on GPU. Parallelization is performed through the CUDA programming technology. We developed data structures that allow for efficient parallelization of GPU calculations. The experimental results show the high performance of the proposed algorithm.