An Experimental Evaluation of Nearest Neighbour Time Series Classification

Bagnall, Anthony and Lines, Jason ORCID: https://orcid.org/0000-0002-1496-5941 (2014) An Experimental Evaluation of Nearest Neighbour Time Series Classification.

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Abstract

Data mining research into time series classification (TSC) has focussed on alternative distance measures for nearest neighbour classifiers. It is standard practice to use 1-NN with Euclidean or dynamic time warping (DTW) distance as a straw man for comparison. As part of a wider investigation into elastic distance measures for TSC~\cite{lines14elastic}, we perform a series of experiments to test whether this standard practice is valid. Specifically, we compare 1-NN classifiers with Euclidean and DTW distance to standard classifiers, examine whether the performance of 1-NN Euclidean approaches that of 1-NN DTW as the number of cases increases, assess whether there is any benefit of setting $k$ for $k$-NN through cross validation whether it is worth setting the warping path for DTW through cross validation and finally is it better to use a window or weighting for DTW. Based on experiments on 77 problems, we conclude that 1-NN with Euclidean distance is fairly easy to beat but 1-NN with DTW is not, if window size is set through cross validation.

Item Type:	Article
Uncontrolled Keywords:	time series classification,dynamic time warping
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Smart Emerging Technologies
Related URLs:	http://uk.arxiv.org/abs/1406.4757
Depositing User:	Pure Connector
Date Deposited:	25 Jul 2014 15:22
Last Modified:	03 May 2023 00:09
URI:	https://ueaeprints.uea.ac.uk/id/eprint/49611
DOI:

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