Holt's exponential smoothing and neural network models for forecasting interval-valued time series
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.
Year of publication: |
2011
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Authors: | Maia, André Luis Santiago ; Carvalho, Francisco de A.T. de |
Published in: |
International Journal of Forecasting. - Elsevier, ISSN 0169-2070. - Vol. 27.2011, 3, p. 740-759
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Publisher: |
Elsevier |
Keywords: | Symbolic data analysis Exponential smoothing Neural networks Hybrid forecasting models Interval-valued data |
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