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

  • Liu, Shen
  • Maharaj, Elizabeth Ann
  • Inder, Brett
Registered author(s):

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.

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Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 70 (2014)
Issue (Month): C ()
Pages: 345-361

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Handle: RePEc:eee:csdana:v:70:y:2014:i:c:p:345-361
Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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