期刊名称：Scientific Annals of the “Alexandru Ioan Cuza” University of Iasi – Economic Sciences Series
印刷版ISSN：2501-1960
电子版ISSN：2501-3165
出版年度：2019
卷号：66
期号：3
页码：363-388
DOI：10.2478/saeb-2019-0027
出版社：Sciendo
摘要：When the literature regarding applications of neural networks is investigated, it appears that a substantial issue is what size the training data should be when modelling a time series through neural networks. The aim of this paper is to determine the size of training data to be used to construct a forecasting model via a multiple-breakpoint test and compare its performance with two general methods, namely, using all available data and using just two years of data. Furthermore, the importance of the selection of the final neural network model is investigated in detail. The results obtained from daily crude oil prices indicate that the data from the last structural change lead to simpler architectures of neural networks and have an advantage in reaching more accurate forecasts in terms of MAE value. In addition, the statistical tests show that there is a statistically significant interaction between data size and stopping rule.
