%0 Journal Article
%T Oil Price Forecasting Based on EMD and BP_AdaBoost Neural Network
%A Huifang Qu
%A Guoqiang Tang
%A Qiying Lao
%J Open Journal of Statistics
%P 660-669
%@ 2161-7198
%D 2018
%I Scientific Research Publishing
%R 10.4236/ojs.2018.84043
%X 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.
%K Empirical Mode Decomposition (EMD)
%K BP_AdaBoost Model
%K Oil Price
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=86450