Crude Oil Price Time Series Forecasting: A Novel Approach Based on Variational Mode Decomposition, Time-Series Imaging, and Deep Learning

Zi-Jian Peng · Chuan Zhang · Yu‐Xin Tian

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

Accurately forecasting crude oil prices is challenging due to market noise and non-stationarity. To address these challenges, we propose a novel forecasting framework that incorporates variational mode decomposition (VMD), time-series imaging, and bidirectional gated recurrent unit network (BGRU). Our approach eliminates additional assumptions and auxiliary data. The framework includes several stages. Firstly, the raw data are preprocessed through normalization, followed by decomposing multiple stationary sub-series through VMD. Subsequently, three time-series imaging techniques, recurrence plot (RP), Gramian angular field (GAF), and Markov transition field (MTF), are employed respectively to transform the sub-series into two-dimensional images. A convolutional neural network (CNN) is then used to extract features from these images. Finally, the extracted features are fed into BGRU for prediction, and the Adam optimizer is used to train models. Experimental evaluations are conducted using a dataset from the U.S. Energy Information Administration, consisting of weekly spot prices FOB of the Oklahoma Cushing WTI crude oil, spanning from June 19, 1998, to May 12, 2023. Results demonstrate that all three hybrid models constructed following our approach outperform benchmark methods. Specifically, our VMD-RP-BGRU model achieves the best forecasting performance with MAE=2.429, MSE=10.94, MAPE=2.94%, and R-squared=0.9418. It exhibits reductions of 21.64%, 23.15%, and 36.46% in MAE, MSE, and MAPE, respectively, compared to the seasonal autoregressive integrated moving average (SARIMA) model, and reductions of 21.18%, 22.70%, and 36.08% compared to the Holt-Winters exponential smoothing (HWES) model. Our study contributes to the advancement of crude oil price forecasting techniques and supports informed decision-making in the energy sector.

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n.d.Forecasting principles and practice 2017Deep learning with long short-term memory networks for financial market predictions 2018Financial time series forecasting model based on CEEMDAN and LSTM 2022Carbon price forecasting based on CEEMDAN and LSTM 2018ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module 2021A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction 2018Text-based crude oil price forecasting: A deep learning approach 2014Forecasting the volatility of crude oil futures using intraday data

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