Polarization of forecast densities : a new approach to time series classification

Liu, Shen, Maharaj, Elizabeth Ann, & Inder, Brett (2014) Polarization of forecast densities : a new approach to time series classification. Computational Statistics & Data Analysis, 70, pp. 345-361.

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Abstract

Time series classification has been extensively explored in many fields of study. Most methods are based on the historical or current information extracted from data. However, if interest is in a specific future time period, methods that directly relate to forecasts of time series are much more appropriate. An approach to time series classification is proposed based on a polarization measure of forecast densities of time series. By fitting autoregressive models, forecast replicates of each time series are obtained via the bias-corrected bootstrap, and a stationarity correction is considered when necessary. Kernel estimators are then employed to approximate forecast densities, and discrepancies of forecast densities of pairs of time series are estimated by a polarization measure, which evaluates the extent to which two densities overlap. Following the distributional properties of the polarization measure, a discriminant rule and a clustering method are proposed to conduct the supervised and unsupervised classification, respectively. The proposed methodology is applied to both simulated and real data sets, and the results show desirable properties.

Impact and interest:

6 citations in Scopus
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4 citations in Web of Science®

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ID Code: 73236
Item Type: Journal Article
Refereed: Yes
Keywords: Time series classification, Forecast densities, Bias-corrected bootstrap, Polarization measures
DOI: 10.1016/j.csda.2013.10.008
ISSN: 0167-9473
Subjects: Australian and New Zealand Standard Research Classification > ECONOMICS (140000) > ECONOMETRICS (140300) > Time-Series Analysis (140305)
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Elsevier B.V.
Copyright Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, [Volume 70, (February 2014)] DOI: 10.1016/j.csda.2013.10.008
Deposited On: 02 Jul 2014 23:56
Last Modified: 05 Feb 2016 06:02

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