MAF: A Method for Detecting Temporal Associations from Multiple Event Sequences

Abstract

In this paper, we propose a two-phase method, called Multivariate Association Finder (MAF), to mine temporal associations in multiple event sequences. It is assumed that a set of event sequences, where each event has an id and an occurrence time, is collected from an application. Our work is motivated by the observation that many associated events in multiple temporal sequences do not occur concurrently but sequentially. In an empirical study, we apply our method to two different application domains. Firstly, we use MAF to detect multivariate motifs from multiple time series data. Existing approaches all assume that the univariate elements of a multivariate motif occur synchronously. The experimental results on both synthetic and read data sets show that our method finds both synchronous and non-synchronous multivariate motifs. Secondly, we apply our method to mine frequent episodes from event streams. Current methods often ask users to provide possible lengths of frequent episodes. The results on neuronal spike simulation data show that MAF automatically detects episodes with variable time delays.