Genetic time series motif discovery for time series classification

Abstract

Time series is a sequence of continuous data and unbounded group of observations found in many applications. Time series motif discovery is an essential and important task in time series mining. Several algorithms have been proposed to discover motifs in time series. These algorithms require user-defined parameters such as length of the motif, support or confidence. However, selection of these parameters is not an easy issue. To overcome the challenge, this paper proposes a genetic algorithm with constraints to discover good trade-off between representative and interesting motif. The discovered motifs are validated for their potential interest in time series classification problem using nearest neighbour classifier. Extensive experiments show that the proposed approach can efficiently discover motifs with different length and to be more accurate and statistically significant than state-of-the-art time series techniques. Finally, the paper demonstrates the efficiency of motif discovery in large medical data from MIT-BIH Arrhythmia database to classify the abnormal signals.