Fast Relevance Discovery in Time Series

Abstract

In this paper, we propose to model time series from a new angle: state transition points. When fluctuation of values in a time series crosses a certain point, it may trigger state transition in the system, which may lead to abrupt changes in many other time series. The concept of state transition points is essential in understanding the behavior of the time series and the behavior of the system. The new measure is robust and is capable of discovering correlations that Pearson's coefficient cannot reveal. We propose efficient algorithms to identify state transition points and to compute correlation between two time series. We also introduce some triangular inequalities to efficiently find highly correlated time series among many time series.