DMCA
Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures
Cached
Download Links
| Citations: | 141 - 24 self |
BibTeX
@MISC{Ding_queryingand,
author = {Hui Ding and Goce Trajcevski and Xiaoyue Wang and Eamonn Keogh},
title = {Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
The last decade has witnessed a tremendous growths of interests in applications that deal with querying and mining of time series data. Numerous representation methods for dimensionality reduction and similarity measures geared towards time series have been introduced. Each individual work introducing a particular method has made specific claims and, aside from the occasional theoretical justifications, provided quantitative experimental observations. However, for the most part, the comparative aspects of these experiments were too narrowly focused on demonstrating the benefits of the proposed methods over some of the previously introduced ones. In order to provide a comprehensive validation, we conducted an extensive set of time series experiments re-implementing 8 different representation methods and 9 similarity measures and their variants, and testing their effectiveness on 38 time series data sets from a wide variety of application domains. In this paper, we give an overview of these different techniques and present our comparative experimental findings regarding their effectiveness. Our experiments have provided both a unified validation of some of the existing achievements, and in some cases, suggested that certain claims in the literature may be unduly optimistic. 1.
Keyphrases
time series data distance measure experimental comparison similarity measure specific claim comparative experimental finding particular method last decade comprehensive validation different technique dimensionality reduction time series data set unified validation towards time series numerous representation method quantitative experimental observation application domain certain claim occasional theoretical justification time series experiment different representation method individual work comparative aspect wide variety tremendous growth extensive set
