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

André Luis Santiago Maia and Francisco de A.T. de Carvalho

International Journal of Forecasting, 2011, vol. 27, issue 3, 740-759

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.

Keywords: Symbolic; data; analysis; Exponential; smoothing; Neural; networks; Hybrid; forecasting; models; Interval-valued; data (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:27:y::i:3:p:740-759

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