A deep learning ensemble approach for crude oil price forecasting - Zhao, Yang; Li, Jianping; Yu, Lean (Energy economics, 2017)
As crude oil price is influenced by numerous factors, capturing its behavior precisely is quite challenging, and thus leads to the difficulty of forecasting. In this study, a deep learning ensemble approach is proposed to deal with this problem. In our approach, two techniques are utilized. One is an advanced deep neural network model named stacked denoising autoencoders (SDAE) which is used to model the nonlinear and complex relationships of oil price with its factors. The other is a powerful ensemble method named bootstrap aggregation (bagging) which generates multiple data sets for training a set of base models (SDAEs). Our approach combines the merits of these two techniques and is especially suitable for oil price forecasting. In the empirical study, the WTI crude oil price series are investigated and 198 economic series are used as exogenous variables. Our approach is tested against some competing approaches and shows superior forecasting ability that is statistically proved by three tests. •A deep learning and ensemble learning based forecasting approach is proposed.•198 exogenous variables are included.•Stacked denoising autoencoders (SDAE) is used to model and forecast oil price.•Bootstrapping aggregation (bagging) is used to grow an ensemble.•Effectiveness of the proposed model is statistically confirmed.