Multiresolution Motif Discovery in Time Series

12 years 16 hour ago
Multiresolution Motif Discovery in Time Series
Time series motif discovery is an important problem with applications in a variety of areas that range from telecommunications to medicine. Several algorithms have been proposed to solve the problem. However, these algorithms heavily use expensive random disk accesses or assume the data can fit into main memory. They only consider motifs at a single resolution and are not suited to interactivity. In this work, we tackle the motif discovery problem as an approximate Top-K frequent subsequence discovery problem. We fully exploit state of the art iSAX representation multiresolution capability to obtain motifs at different resolutions. This property yields interactivity, allowing the user to navigate along the Top-K motifs structure. This permits a deeper understanding of the time series database. Further, we apply the Top-K space saving algorithm to our frequent subsequences approach. A scalable algorithm is obtained that is suitable for data stream like applications where small memory...
Nuno Castro, Paulo J. Azevedo
Added 30 Jan 2011
Updated 30 Jan 2011
Type Journal
Year 2010
Where SDM
Authors Nuno Castro, Paulo J. Azevedo
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