Abstract—This study proposes to use a hybrid ensemble learning approach to improve the prediction efficiency of crude oil prices. It combines the Long Short-Term Memory (LSTM) with factors that influence the price of crude oil. The information from fundamental and technical indicators is considered along with statistical model predictions like autoregressive integrated moving average (ARIMA)to make one-step-ahead crude oil price predictions. A Principal Component Analysis (PCA) approach is employed to transform the explanatory variables. This study combines the LSTM with PCA, jointly known as the LP model wherein PCA transforms of the fundamental and technical indicators are used as inputs to improve LSTM predictions. Further, it attempts to improve these predictions by introducing the LSTM+PCA+ARIMA (LPA) model, which uses an ensemble learning approach to utilize the forecast from the ARIMA model, as an additional input. Among LP and LPA models, the LSTM model is used as a benchmark to evaluate the performance of the hybrid models. Based on the result, a significant improvement is seen in the LP model over the chosen window sizes and error metrics. On the other hand, the LPA model performs better across all dimensions with an average improvement of 41% over the LSTM model in terms of forecasting accuracy. Moreover, the equivalence of forecasting accuracy is tested using the Diebold-Mariano and Wilcoxon signed-rank tests.
 
Keywords—Long Short-Term Memory (LSTM), Principal Component Analysis (PCA), ensemble learning, crude oil, forecasting


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