Holt's exponential smoothing and neural network models for forecasting interval-valued time series
AbstractInterval-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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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 27 (2011)
Issue (Month): 3 (July)
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Web page: http://www.elsevier.com/locate/ijforecast
Symbolic data analysis Exponential smoothing Neural networks Hybrid forecasting models Interval-valued data;
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