Crude oil is an essential commodity for industry and the prediction of its price is crucial for many business entities and government organizations. While there have been quite a few conventional statistical models to forecast oil prices, we find that there is not much research using decision tree models to predict crude oil prices. In this research, we develop decision tree models to forecast crude oil prices. In addition to historical crude oil price time series data, we also use some predictor variables that would potentially affect crude oil prices, including crude oil demand and supply, and monthly GDP and CPI during the period 1992 through 2017 with a total of 312 observations. In this research, we use decision tree models to predict crude oil price. We find that the decision tree models developed in this research are expected to have higher forecasting accuracy than that of such benchmark models as multiple linear regression and time series autoregressive integrated moving average (ARIMA).
Chen, Engu and He, Xin James
"Crude Oil Price Prediction with Decision Tree Based Regression Approach,"
Journal of International Technology and Information Management: Vol. 27:
4, Article 1.
Available at: https://scholarworks.lib.csusb.edu/jitim/vol27/iss4/1
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