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Abstract
Since the commodity and financial attributes of crude oil will have a long-term or short-term impact on crude oil prices, we propose a de-dimension machine learning model approach to forecast the international crude oil prices. First, we use principal component analysis (PCA), multidimensional scale (MDS), and locally linear embedding (LLE) methods to reduce the dimensions of the data. Then, based on the recurrent neural network (RNN) and long-term and short-term memory (LSTM) models, we build eight models for predicting the future and spot prices of international crude oil. From the analysis and comparison of the prediction results, we find that reducing the dimension of the data can improve the accuracy of the model and the applicability of RNN and LSTM models. In addition, the LLE-RNN/LSTM models can most successfully capture the nonlinear characteristics of crude oil prices. When the moving window size is twenty, that is, when crude oil price data are lagging by almost a month, each model can minimize its error, and the LLE-RNN /LSTM models have the best robustness.
Highlights
Forecasting the price of crude oil is of great significance for energy policy-makers, market participants, portfolio diversification, and energy risk management. ere are many factors influencing the crude oil price, and the influence period of each factor on the crude oil prices is not consistent, so the crude oil prices have nonlinear characteristics [1, 2]
Identifying the formation process of crude oil prices is of significance for accurate prediction, but this process is complicated. erefore, we try to use the machine learning methods to deal with the vague influence among various factors. e formation process of crude oil prices can lead the traditional econometrics model to have a large error in crude oil price prediction, but the recurrent neural network (RNN) and long-term and short-term memory (LSTM) models can fit well
RNN and LSTM models are used as the benchmark model to predict the crude oil prices. ird, considering that too many influencing factors are likely to cause overfitting, the de-dimension method is adopted to improve the accuracy of the model. at is, we propose a de-dimension machine learning model approach to forecast the international crude oil prices by taking account of the dual attributes of oil
Summary
Forecasting the price of crude oil is of great significance for energy policy-makers, market participants, portfolio diversification, and energy risk management. ere are many factors influencing the crude oil price, and the influence period of each factor on the crude oil prices is not consistent, so the crude oil prices have nonlinear characteristics [1, 2]. Chen et al [30] showed that this nonlinear relationship is not significant, but under the supply and demand driven price shocks, the dollar exchange rate has caused different impacts on the crude oil prices. There are many factors influencing the fluctuation of crude oil prices, including crude oil supply and demand, substitute prices, dollar exchange rate, money supply, and gold prices. The above variables are selected as the factors affecting the international crude oil prices and substituted into the model for empirical analysis
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