Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks

Abstract

Accurate prediction of crude oil prices is meaningful for reducing firm risks, stabilizing commodity prices and maintaining national financial security. Wrong crude oil price forecasts can bring huge losses to governments, enterprises, investors and even cause economic and social instability. Many classic econometrics and computational approaches show good performance for the ordinary time series prediction tasks, but not satisfactory in crude oil price predictions. They ignore the characteristics of non-linearity and non-stationarity of crude oil prices data, which hinder an accurate prediction and eventually lead to poor accuracy or the wrong result. Empirical mode decomposition (EMD) and ensemble EMD (EEMD) solve the problems of non-stationary time series forecasting, but they also generate new problems of mode mixing and reconstruction errors. We propose a hybrid method that is combination of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer gated recurrent unit (ML-GRU) neural network to solve the abovementioned issues. This not only deals with the issue of mode mixing effectively, but also makes the reconstruction error of data close to zero. Multi-layer GRU has an excellent ability of nonlinear data-fitting. The experimental results of real WTI crude oil dataset show that the proposed approach perform better in crude oil prices forecasts than some state-of-the-art models.

A New Approach for Forecasting Crude Oil Prices Using Median Ensemble Empirical Mode Decomposition and Group Method of Data Handling

“…China's oil consumption, for instance, has risen from an average of 69,700 barrels per day in 2005 to 145,100 barrels per day in 2019. As a demand factor, rising crude oil prices would result in higher production costs for the nonoil companies and a shrink in profit [2]. As crude oil is a very critical commodity to the global economy, many leading governments, investors, and scholars have invested a lot of effort in building models to predict fluctuations in their prices and important properties.…”

“…ey concluded that the hybrid model improves the volatility prediction accuracy by more than 30% through the results measured by the heteroscedasticity-adjusted mean square error (HMSE) model. Lin and Sun [2] used CEEMDAN-MLGRU based decomposition method to forecast WTI crude oil price. Li et al [18] used a new hybridbased model, namely, EEMD-SBL-ADD, and concluded that the proposed model is promising for forecasting crude oil price.…”

Using Artificial Neural Networks to Find Buy Signals for WTI Crude Oil Call Options

“…Studies show that models using artificial neural networks (ANNs) to forecast oil prices provide an alternative to the classical econometric models, such as autoregressive integrated moving average (ARIMA) or (generalized) autoregressive conditional heteroscedasticity (ARCH, GARCH) models [21][22][23][24][25][26][27][28][29]. These econometric models point out such features as changes in variance over time or the seasonal nature of variability and improve prediction accuracy mainly based on the assumption of stationary and linearity of the analyzed time series [30]. Nonetheless, the complexity of the relations between crude oil and other markets, as well as the macroeconomic variables causes oil prices to be characterized by non-linearity and non-stationarity [31], hence there are attempts to use artificial intelligence techniques, including the above-mentioned artificial neural networks.…”

Short-term wind speed prediction based on CEEMDAN-SE-improved PIO-GRNN model

“…Compared with EEMD, the adaptive gaussian white noise is appended in each stage through the CEEMDAN. [12][13][14] Each modal component is obtained through operating the unique residual signal, which effectively figures out the problem of low decomposition efficiency for EEMD and the difficulty of completely eliminating the noise.…”