Three-way Drift Detection for State Transition Pattern on Multivariate Time Series

Abstract

Unsupervised drift detection for multivariate time series (MTSs) is an important task in machine learning.However, this issue is challenging because the definitions of sequential patterns and their drifts are very flexible.Inspired by the idea of “Think in Threes”, this paper proposes a three-way drift detection method for state transition pattern with periodic wildcard gaps (3WDD-STAP), which is improved from the incremental mining algorithm of STAP.Without additional parameters, both frequent and drifted STAPs can be obtained simultaneously.Considering the support changes around the increments, we define three types of STAP drift.Type I drift indicates that STAPs change from frequent to infrequent.The incremental dataset needs to be rescanned.Type II drift indicates that STAPs change from infrequent to frequent.The original dataset needs to be rescanned.Type III drift indicates that STAPs retain frequent or infrequent, namely, these STAPs are normal.No dataset needs to be rescanned.Finally, experimental results on 2 real-world datasets show that:1)we obtain less drifted STAPs with less α and β, and vice versa;2)the two types of drifted STAPs obeys different distribution for various datasets;3)the obtained STAPs and their drifts have strong readability.

Keywords

• multivariate time series|anomaly detection|think in threes|sequential pattern discovery|incremental learning