How to extract meaningful shapes from noisy time-series subsequences?

Abstract

A method for extracting and classifying shapes from noisy time series is proposed. The method consists of two steps. The first step is to perform a noise test on each subsequence extracted from the series using a sliding window. All the subsequences recognised as noise are removed from further analysis, and the shapes are extracted from the remaining non… (More)
DOI: 10.1109/CIDM.2013.6597219

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