Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations

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Forecasting Crude Oil Prices: a Deep Learning based Model (2017, methodology): “[4] used the Recurrent Neural Network (RNN) to forecast the crude oil indices.”
Recurrent Neural Networks Approach to the Financial Forecast of Google Assets (2017, neutral): “Even taking into account only the NNS ones, we can applications belonging to the MLPs methods, see, e.g., [16], convolutional neural networks (CNNs), see, e.g.,[6], Elman neural networks, see, e.g., [10], etc.”
Machine learning in energy economics and finance: A review (2019, background): “Wang and Wang (2016) propose an ANN architecture combining multilayer perceptron and Elman Recurrent Neural Networks (ERNNs), which is a timevarying predictive control system, along with stochastic time effective function.”
The VEC-NAR model for short-term forecasting of oil prices (2019, background): “Mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) are commonly used to estimate the predictive performance of forecasting models (Cheong, 2009; Mohammadi and Su, 2010; Movagharnejad et al., 2011; Jammazi and Aloui, 2012; Suganthia and Samuel, 2012; Xu and Ouennich”