Visualizing Variable-Length Time Series Motifs
نویسندگان
چکیده
The problem of time series motif discovery has received a lot of attention from researchers in the past decade. Most existing work on finding time series motifs require that the length of the motifs 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 develop a motif visualization system based on grammar induction. We demonstrate that grammar induction in time series can effectively identify repeated patterns without prior knowledge of their lengths. The motifs discovered by the visualization system are variablelengths in two ways. Not only can the inter-motif subsequences have variable lengths, the intra-motif subsequences also are not restricted to have identical length—a unique property that is desirable, but has not been seen in the literature.
منابع مشابه
Efficient Discovery of Variable-length Time Series Motifs with Large Length Range in Million Scale Time Series
Detecting repeated variable-length patterns, also called variable-length motifs, has received a great amount of attention in recent years. Current state-of-the-art algorithm utilizes fixed-length motif discovery algorithm as a subroutine to enumerate variable-length motifs. As a result, it may take hours or days to execute when enumeration range is large. In this work, we introduce an approxima...
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Detecting repeated variable-length patterns, also called variable-length motifs, has received a great amount of attention in recent years. Current state-of-the-art algorithm utilizes fixed-length motif discovery algorithm as a subroutine to enumerate variable-length motifs. As a result, it may take hours or days to execute when enumeration range is large. In this work, we introduce an approxima...
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تاریخ انتشار 2012