Open AccessData Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks
Author(s) -
Serkan Aras,
Manel Hamdi
Publication year - 2019
Publication title -
scientific annals of economics and business
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.204
H-Index - 6
eISSN - 2501-3165
pISSN - 2501-1960
DOI - 10.47743/saeb-2019-0027
Subject(s) - artificial neural network , computer science , crude oil , econometrics , test data , value (mathematics) , construct (python library) , artificial intelligence , machine learning , mathematics , engineering , petroleum engineering , programming language
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.