Discovery of Temporal Dependencies between Frequent Patterns in Multivariate Time Series

Abstract

We consider the problem of mining multivariate time series data for discovering (i) frequently occurring substring patterns in a dimension, (ii) temporal associations among these substring patterns within or across different dimensions, and (iii) large intervals that sustain a particular mode of operation. These represent patterns at three different levels of abstraction for a dataset having very fine granularity. Discovery of such temporal associations in a multivariate setting provides useful insights which results in a prediction and diagnostic capability for the domain. In this paper we present a methodology for efficiently discovering all frequent patterns in each dimension of the data using Suffix Trees; then clustering these substring patterns to construct equivalence classes of similar (approximately matching) patterns; and then searching for temporal dependencies among these equivalence classes using an efficient search algorithm. Modes of operation are then inferred as summarization of these temporal dependencies. Our method is generalizable, scalable, and can be adapted to provide robustness against noise, shifting, and scaling factors