A novel clustering-based method for time series motif discovery under time warping measure

Abstract

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. There has been one proposed method, called MDTW_WedgeTree, for time series motif discovery under DTW distance. But this method aims to deal with the case in which motif is the time series in a time series database which has the highest count of its similar time series within a range r. To adapt the above-mentioned method to the case in which motifs are frequently occurring subsequences of a longer time series, we modify MDTW_WedgeTree to a new algorithm for discovering "subsequence" motifs in time series under DTW. The proposed method consists of a segmentation method to divide the time series into motif candidates and a BIRCHbased clustering which can efficiently cluster motif candidate subsequences under DTW distance. Experimental results showed that our proposed method for discovering "subsequence" motifs performs very efficiently on large time series datasets while brings out high accuracy.

Iterative multiscale dynamic time warping (IMs-DTW): a tool for rainfall time series comparison

“…This work was primarily dedicated to the ability to use DTW algorithms to provide a pertinent measure of dissimilarity for rainfall time series. In future works, we will focus on applications using the IMs-DTW in the framework of precipitation such as rain cell tracking or rain events clustering [34,37]. The code source (in Python) and rain gauges data set are available on the GitHub deposit at the following address: https://github.com/djallelDILMI/IMs-DTW.…”