Combining temporal disaggregation forecasts with artificial neural networks
by Leila Hedhili Zaier
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 9, No. 4, 2014

Abstract: This paper investigates the use of artificial neural networks (ANNs) to combine time series forecast of high frequency data obtained from various temporal disaggregation methods without related series (e.g., Almon, 1988; Lisman and Sandee, 1964; Boot and Feibes, 1967; Boot et al., 1967; Stram and Wei, 1986; Zaier and Abed, 2014). We use the example of deriving quarterly US GDP from annual one to evaluate the performance of the proposed method. We demonstrate that combining with nonlinear ANNs generally produces better forecasts than forecasts obtained from individual temporal disaggregation methods and from also traditional linear combining procedures on the basis of performance measures.

Online publication date: Fri, 10-Apr-2015

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