University of Oulu

Optimization in semi-supervised classification of multivariate time series

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Author: Lintonen, Timo1
Organizations: 1University of Oulu, Faculty of Science, Mathematics
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
Pages: 92
Persistent link:
Language: English
Published: Oulu : T. Lintonen, 2019
Publish Date: 2019-02-13
Thesis type: Master's thesis
Tutor: Laitinen, Erkki
Reviewer: Laitinen, Erkki
Ruha, Leena


In this thesis, I study methods that classify time series in a semi-supervised manner. I compare the performance of models that assume independent and identically distributed observations against models that assume nearby observations to be dependent of each other. These models are evaluated on three real world time series data sets. In addition, I carefully go through the theory of mathematical optimization behind two successful algorithms used in this thesis: Support Vector Data Description and Dynamic Time Warping. For the algorithm Dynamic Time Warping, I provide a novel proof that is based on dynamic optimization.

The experiments in this thesis suggest that the assumption of observations in time series to be independent and identically distributed may deteriorate the results of semi-supervised classification. The novel self-training method presented in this thesis called Peak Evaluation using Perceptually Important Points shows great performance on multivariate time series compared to the methods currently existing in literature. The feature subset selection of multivariate time series may improve classification performance, but finding a reliable unsupervised feature subset selection method remains an open question.

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Copyright information: © Timo Lintonen, 2019. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.