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

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  • Maia, André Luis Santiago
  • de Carvalho, Francisco de A.T.

Abstract

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.

Suggested Citation

  • Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt’s exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759.
    • Handle: RePEc:eee:intfor:v:27:y:2011:i:3:p:740-759
    DOI: 10.1016/j.ijforecast.2010.02.012
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