Discovery of time series $$k$$ k -motifs based on multidimensional index

Abstract

Time series motifs are frequently occurring but previously unknown subsequences of a longer time series. Discovering time series motifs is a crucial task in time series data mining. In time series motif discovery algorithm, finding nearest neighbors of a subsequence is the basic operation. To make this basic operation efficient, we can make use of some advanced multidimensional index structure for time series data. In this paper, we propose two novel algorithms for discovering motifs in time series data: The first algorithm is based on $$\hbox {R}^{*}$$R?-tree and early abandoning technique and the second algorithm makes use of a dimensionality reduction method and state-of-the-art Skyline index. We demonstrate that the effectiveness of our proposed algorithms by experimenting on real datasets from different areas. The experimental results reveal that our two proposed algorithms outperform the most popular method, random projection, in time efficiency while bring out the same accuracy.