A Fast Method for Motif Discovery in Large Time Series Database under Dynamic Time Warping

Abstract

Finding similar time series in a time series database is one of the important problems in time series data mining. The time series which has the highest count of its similar time series within a range r in a time series database is called the 1-motif. The problem of time series motif discovery has attracted a lot of attention and is useful in many real world applications. However, most of the proposed methods so far use Euclidean distance to deal with this problem. In this work, we propose a fast method for time series motif discovery which uses Dynamic Time Warping distance, a better measure than Euclidean distance. Experimental results showed that our proposed motif discovery method performs very efficiently on large time serried datasets while brings out high accuracy.