Approximate variable-length time series motif discovery using grammar inference

Authors:
Yuan Li;Jessica Lin
Affiliations:
George Mason University, Fairfax, VA;George Mason University, Fairfax, VA
Venue:
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Year:
2010

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Abstract

The problem of identifying frequently occurring patterns, or motifs, in time series data has received a lot of attention in the past few years. Most existing work on finding time series motifs require that the length of the patterns be known in advance. However, such information is not always available. In addition, motifs of different lengths may co-exist in a time series dataset. In this work, we propose a novel approach, based on grammar induction, for approximate variable-length time series motif discovery. Our algorithm offers the advantage of discovering hierarchical structure, regularity and grammar from the data. The preliminary results are promising. They show that the grammar-based approach is able to find some important motifs, and suggest that the new direction of using grammar-based algorithms for time series pattern discovery might be worth exploring.