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Univariate and Multivariate Time Series Manifold Learning

O’Reilly, Colin, Moessner, Klaus and Nati, M (2017) Univariate and Multivariate Time Series Manifold Learning Knowledge-Based Systems, 133. pp. 1-16.

Univariate and Multivariate Time Series Manifold Learning.pdf - Accepted version Manuscript

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Time series analysis aims to extract meaningful information from data that has been generated in sequence by a dynamic process. The modelling of the non-linear dynamics of a signal is often performed using a linear space with a similarity metric which is either linear or attempts to model the non-linearity of the data in the linear space. In this research, a different approach is taken where the non-linear dynamics of the time series are represented using a phase space. Training data is used to construct the phase space in which the data lies on or close to a lower-dimensional manifold. The basis of the non-linear manifold is derived using the kernel principal components derived using kernel principal component analysis where fewer components are retained in order to identify the lower-dimensional manifold. Data instances are projected onto the manifold, and those with a large distance between the original point and the projection are considered to be derived from a different underlying process. The proposed algorithm is able to perform time series classification on univariate and multivariate data. Evaluations on a large number of real-world data sets demonstrate the accuracy of the new algorithm and how it exceeds state-of-the-art performance.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
O’Reilly, Colin
Nati, M
Date : 29 May 2017
DOI : 10.1016/j.knosys.2017.05.026
Copyright Disclaimer : © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Uncontrolled Keywords : time series, univariate, multivariate, one-class classification, kernel principal component analysis
Depositing User : Melanie Hughes
Date Deposited : 30 May 2017 09:54
Last Modified : 16 Jan 2019 18:52

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