Document Type

Article

Publication Date

5-17-2018

Publication Title

IEEE Access

First page number:

1

Last page number:

10

Abstract

Several methods exist in classification literature to quantify the similarity between two time series data sets. Applications of these methods range from the traditional Euclidean type metric to the more advanced Dynamic Time Warping metric. Most of these adequately address structural similarity but fail in meeting goals outside it. For example, a tool that could be excellent to identify the seasonal similarity between two time series vectors might prove inadequate in the presence of outliers. In this paper, we have proposed a unifying measure for binary classification that performed well while embracing several aspects of dissimilarity. This statistic is gaining prominence in various fields, such as geology and finance, and is crucial in time series database formation and clustering studies.

Keywords

Bootstrapping; Classification; Database clustering; Empirical recurrence rates; Empirical recurrence rates ratios; Similarity measures; Time series

Disciplines

Computer Sciences

Language

English

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