Journal
KNOWLEDGE-BASED SYSTEMS
Volume 61, Issue -, Pages 98-108Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2014.02.011
Keywords
Dynamic time warping; Derivative dynamic time warping; Time series; Hilbert transform; Cosine transform; Sine transform
Categories
Ask authors/readers for more resources
Over recent years the popularity of time series has soared. As a consequence there has been a dramatic increase in the amount of interest in querying and mining such data. In particular, many new distance measures between time series have been introduced. In this paper we propose a new distance function based on derivatives and transforms of time series. In contrast to well-known measures from the literature, our approach combines three distances: DTW distance between time series, DTW distance between derivatives of time series, and DTW distance between transforms of time series. The new distance is used in classification with the nearest neighbor rule. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 47 time series data sets from a wide variety of application domains. Our experiments show that this new method provides a significantly more accurate classification on the examined data sets. (c) 2014 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available