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Holt's exponential smoothing and neural network models for forecasting interval-valued time series

  • Maia, André Luis Santiago
  • de Carvalho, Francisco de A.T.
Registered author(s):

    Interval-valued time series are interval-valued data that are collected in a chronological sequence over time. This paper introduces three approaches to forecasting interval-valued time series. The first two approaches are based on multilayer perceptron (MLP) neural networks and Holt's exponential smoothing methods, respectively. In Holt's method for interval-valued time series, the smoothing parameters are estimated by using techniques for non-linear optimization problems with bound constraints. The third approach is based on a hybrid methodology that combines the MLP and Holt models. The practicality of the methods is demonstrated through simulation studies and applications using real interval-valued stock market time series.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0169207010000506
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    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 27 (2011)
    Issue (Month): 3 (July)
    Pages: 740-759

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    Handle: RePEc:eee:intfor:v:27:y::i:3:p:740-759
    Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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