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An empirical evaluation of kernels for time series

There exist a variety of distance measures which operate on time series kernels. The objective of this article is to compare those distance measures in a support vector machine setting. A support vector machine is a state-of-the-art classifier for static (non-time series) datasets and usually outper...

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Autores principales: Badiane, Mourtadha, Cunningham, Pádraig
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8933385/
https://www.ncbi.nlm.nih.gov/pubmed/35370341
http://dx.doi.org/10.1007/s10462-021-10050-y
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author Badiane, Mourtadha
Cunningham, Pádraig
author_facet Badiane, Mourtadha
Cunningham, Pádraig
author_sort Badiane, Mourtadha
collection PubMed
description There exist a variety of distance measures which operate on time series kernels. The objective of this article is to compare those distance measures in a support vector machine setting. A support vector machine is a state-of-the-art classifier for static (non-time series) datasets and usually outperforms k-Nearest Neighbour, however it is often noted that that 1-NN DTW is a robust baseline for time-series classification. Through a collection of experiments we determine that the most effective distance measure is Dynamic Time Warping and the most effective classifier is kNN. However, a surprising result is that the pairing of kNN and DTW is not the most effective model. Instead we have discovered via experimentation that Dynamic Time Warping paired with the Gaussian Support Vector Machine is the most accurate time series classifier. Finally, with good reason we recommend a slightly inferior (in terms of accuracy) model Time Warp Edit Distance paired with the Gaussian Support Vector Machine as it has a better theoretical basis. We also discuss the reduction in computational cost achieved by using a Support Vector Machine, finding that the Negative Kernel paired with the Dynamic Time Warping distance produces the greatest reduction in computational cost.
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spelling pubmed-89333852022-04-01 An empirical evaluation of kernels for time series Badiane, Mourtadha Cunningham, Pádraig Artif Intell Rev Article There exist a variety of distance measures which operate on time series kernels. The objective of this article is to compare those distance measures in a support vector machine setting. A support vector machine is a state-of-the-art classifier for static (non-time series) datasets and usually outperforms k-Nearest Neighbour, however it is often noted that that 1-NN DTW is a robust baseline for time-series classification. Through a collection of experiments we determine that the most effective distance measure is Dynamic Time Warping and the most effective classifier is kNN. However, a surprising result is that the pairing of kNN and DTW is not the most effective model. Instead we have discovered via experimentation that Dynamic Time Warping paired with the Gaussian Support Vector Machine is the most accurate time series classifier. Finally, with good reason we recommend a slightly inferior (in terms of accuracy) model Time Warp Edit Distance paired with the Gaussian Support Vector Machine as it has a better theoretical basis. We also discuss the reduction in computational cost achieved by using a Support Vector Machine, finding that the Negative Kernel paired with the Dynamic Time Warping distance produces the greatest reduction in computational cost. Springer Netherlands 2021-07-27 2022 /pmc/articles/PMC8933385/ /pubmed/35370341 http://dx.doi.org/10.1007/s10462-021-10050-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Badiane, Mourtadha
Cunningham, Pádraig
An empirical evaluation of kernels for time series
title An empirical evaluation of kernels for time series
title_full An empirical evaluation of kernels for time series
title_fullStr An empirical evaluation of kernels for time series
title_full_unstemmed An empirical evaluation of kernels for time series
title_short An empirical evaluation of kernels for time series
title_sort empirical evaluation of kernels for time series
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8933385/
https://www.ncbi.nlm.nih.gov/pubmed/35370341
http://dx.doi.org/10.1007/s10462-021-10050-y
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