A Hybrid of Box-Jenkins ARIMA Model and Neural Networks for Forecasting South African Crude Oil Prices

被引:0
|
作者
Tsoku, Johannes Tshepiso [1 ]
Metsileng, Daniel [1 ]
Botlhoko, Tshegofatso [1 ]
机构
[1] North West Univ, Dept Business Stat & Operat Res, Mafikeng Campus, ZA-2745 Mmabatho, South Africa
来源
关键词
ANN-based ELM; ARIMA model; crude oil price; forecasting; GRNN; hybrid models; INTEGRATED MOVING AVERAGE; EXTREME LEARNING-MACHINE; TIME-SERIES; ELM; TUBERCULOSIS;
D O I
10.3390/ijfs12040118
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
The current study aims to model the South African crude oil prices using the hybrid of Box-Jenkins autoregressive integrated moving average (ARIMA) and Neural Networks (NNs). This study introduces a hybrid approach to forecasting methods aimed at resolving the issues of lack of precision in forecasting. The proposed methodology includes two models, namely, hybridisation of ARIMA with artificial neural network (ANN)-based Extreme Learning Machine (ELM) and ARIMA with general regression neural network (GRNN) to model both linear and nonlinear simultaneously. The models were compared with the base ARIMA model. The study utilised monthly time series data spanning from January 2021 to March 2023. The formal stationarity test confirmed that the crude oil price series is integrated of order one, I(1). For the linear process, the ARIMA (2,1,2) model was identified as the best fit for the series and successfully passed all diagnostic tests. The ARIMA-ANN-based ELM hybrid model outperformed both the individual ARIMA model and the ARIMA-GRNN hybrid. However, the ARIMA model also showed better performance than the ARIMA-GRNN hybrid, highlighting its strong competitiveness compared to the ARIMA-ANN-based ELM model. The hybrid models are recommended for use by policy makers and practitioners in general.
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页数:13
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