Mid-term forecasting of crude oil prices using the hybrid CEEMDAN and CNN_LSTM deep learning model

Forecasting crude oil prices has always been a matter of discussion among energy experts.Due to a significant dependence of the global economy on crude oil, the volatility of the spot price can impact the supply and demand of the market.Moreover, crude oil is still the primary energy for transportation worldwide.Although renewable energy sources have developed significantly, crude oil has been dominant in transportation fuels in the last few decades.This study focuses on mid-term multi-step forecasting and provides a forecasting model that provides a robust prediction for 60 to 90 steps ahead.Our main objective is to develop a forecasting model that can maintain high accuracy and low errors.Our analysis uses a hybrid Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and the Convolutional Neural Network, Long Short-Term Memory (CNN_LSTM) deep learning model.These three techniques, which have different advantages, are put together, and the combination of them is able to identify features (trend and seasonality) in historical data learning and perform high prediction accuracy for next-term prediction.We compared the proposed model with other decomposition and deep learning techniques.The proposed model shows lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values than other benchmark models for Brent and crude West Texas Intermediate (WTI) oil prices -the proposed model's Mean Absolute Percentage Error (MAPE) results in better forecasting with MAPE values between 4 to 10.The simulation with box plot analysis also gives 20 a quartile range value below 0.2, which shows the stability of the model in each iteration.Finally, the proposed model can provide a robust forecasting model for multi-step mid-term forecasting.