An efficient method for motif and anomaly detection in time series based on clustering

Abstract

Motifs and anomalies are two important representative patterns in a time series. Existing approaches usually handle motif discovery and anomaly detection in time series separately. In this paper, we propose a new efficient clustering-based method for discovering motif and detecting anomaly at the same time in large time series data. Our method first extracts motif/anomaly candidates from a time series by using significant extreme points and then clusters the candidates by using BIRCH algorithm. The proposed method computes anomaly scores for all sub-clusters and discovers the top motif based on the sub-cluster with the smallest anomaly score and detects the top anomaly based on the sub-cluster with the largest anomaly score. Experimental results on several benchmark datasets show that our proposed method can discover precise motif and anomaly with high time efficiency on large time series data.