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-noise subsequences. The second step is to cluster these extracted shapes. Although extracted from subsequences, these shapes form a non-overlapping set of time series subsequences and are hence amenable to meaningful clustering. The method is primarily designed for extracting and classifying shapes from very noisy real-world time series. Tes..