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Deformable Motif Discovery in Time Series by pyrole1 is a document available to read on EtoBox.

This document summarizes a research paper that presents a new probabilistic model called the Continuous Shape Template Model (CSTM) for discovering deformable motifs (repeated patterns that exhibit variability) in continuous time series data. The model defines a hidden Markov model to segment time series data into repeating motifs generated by continuous shape templates, and non-repeating random walks. It learns the shape templates and allowed deformations in an unsupervised manner from unlabeled time serie

Author
pyrole1
Language
EN