Time Series Classification in Dissimilarity Spaces

Abstract

Time series classification in the dissimilarity space combines the advantages of the dynamic time warping and the rich mathematical structure of Euclidean spaces. We applied dimension reduction using PCA followed by support vector learning on dissimilarity representations to 43 UCR datasets. Results indicate that time series classification in dissimilarity space has potential to complement the state-of-the-art.

@inproceedings{DBLP:conf/pkdd/JainS15,
  author    = {Brijnesh J. Jain and
               Stephan Spiegel},
  title     = {Time Series Classification in Dissimilarity Spaces},
  booktitle = {Proceedings of the 1st International Workshop on Advanced Analytics
               and Learning on Temporal Data, {AALTD} 2015, co-located with The European
               Conference on Machine Learning and Principles and Practice of Knowledge
               Discovery in Databases {(ECML} {PKDD} 2015), Porto, Portugal, September
               11, 2015.},
  year      = {2015},
  crossref  = {DBLP:conf/pkdd/2015aaltd},
  url       = {http://ceur-ws.org/Vol-1425/paper11.pdf},
  timestamp = {Fri, 04 Sep 2015 13:28:34 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/pkdd/JainS15},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

Authors:

Brijnesh Jain, Stephan Spiegel

Category:

Conference Paper

Year:

2015

Location:

ECML-PKDD-15: Lecture Notes in Artificial Intelligence (LNAI) Series, Springer