Extended Vertical Lists for Temporal Pattern Mining from Multivariate Time Series

TL;DR

This paper introduces a novel method for temporal pattern mining from multivariate time series that significantly improves computational speed using a new data structure, at the cost of increased memory usage.

Contribution

The paper proposes the Extended Vertical List data structure and an extension of the Apriori property to enhance the efficiency of temporal pattern mining algorithms.

Findings

Method is significantly faster than previous algorithms

Speed-up requires more memory

Effective for complex temporal pattern detection

Abstract

Temporal Pattern Mining (TPM) is the problem of mining predictive complex temporal patterns from multivariate time series in a supervised setting. We develop a new method called the Fast Temporal Pattern Mining with Extended Vertical Lists. This method utilizes an extension of the Apriori property which requires a more complex pattern to appear within records only at places where all of its subpatterns are detected as well. The approach is based on a novel data structure called the Extended Vertical List that tracks positions of the first state of the pattern inside records. Extensive computational results indicate that the new method performs significantly faster than the previous version of the algorithm for TMP. However, the speed-up comes at the expense of memory usage.