Using dynamic time warping distances as features for improved time series classification
Using dynamic time warping distances as features for improved time series classification
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Publisher
New York: Springer US
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Language
English
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Publisher
New York: Springer US
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Dynamic time warping (DTW) has proven itself to be an exceptionally strong distance measure for time series. DTW in combination with one-nearest neighbor, one of the simplest machine learning methods, has been difficult to convincingly outperform on the time series classification task. In this paper, we present a simple technique for time series cl...
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Full title
Using dynamic time warping distances as features for improved time series classification
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TN_cdi_proquest_miscellaneous_1793243842
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_1793243842
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ISSN
1384-5810
E-ISSN
1573-756X
DOI
10.1007/s10618-015-0418-x
