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Visualizing Variable-Length Time Series Motifs
[chapter]
2012
Proceedings of the 2012 SIAM International Conference on Data Mining
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
doi:10.1137/1.9781611972825.77
dblp:conf/sdm/LiLO12
fatcat:briw6g5lrbddder5xyr7wqyn4m