TITLE:
Oil Price Forecasting Based on EMD and BP_AdaBoost Neural Network
AUTHORS:
Huifang Qu, Guoqiang Tang, Qiying Lao
KEYWORDS:
Empirical Mode Decomposition (EMD), BP_AdaBoost Model, Oil Price
JOURNAL NAME:
Open Journal of Statistics,
Vol.8 No.4,
August
3,
2018
ABSTRACT: Empirical mode decomposition (EMD) and BP_AdaBoost neural network are
used in this paper to model the oil price. Based on the benefits of these two
methods, we predict the oil price by using them. To a certain extent, it effectively
improves the accuracy of short-term price forecasting. Forecast results of this
model are compared with the results of the ARIMA model, BP neural network and
EMD-BP combined model. The experimental result shows that the root mean square
error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE)
and Theil inequality (U) of EMD and BP_AdaBoost model are lower than other
models, and the combined model has better prediction accuracy.