Time Series Modelling and Prediction of WTI Crude Oil Prices Trend using Machine Learning Techniques

It is challenging to forecast crude oil prices. Given that the international crude oil price is time-varying and nonlinear, suggest a new mixed method for oil price forecasting. First, the traditional time series technique ARIMA method for predictions is incorporated. Then, The time-varying and nonlinear components of crude oil prices are forecasted using machine learning approaches such as boosted trees, SVM, ANN, LSTM, bagged trees and linear regression, etc. The ultimate predicted outcomes of crude oil prices are then calculated by adding the predicted prices of each component. The results showed that the new proposed hybrid method was capable of a high degree of oil price prediction based on its exceptional ability to adapt sample selection, data frequencies and structural breaks. In addition, the comparisons show that the new method proves to be more accurate than these well-known methods for crude oil price forecasts. The WTI daily data from 1986 to 2018 has been utilised for forecasting of crude oil trend for 2018 in next 50 days. Forecasts were made using the time series forecasting approach with the ARIMA model and the machine learning approach. Validation metrics have been used to evaluate the performance of these models and it was noted that they performed well. The effects of prediction methods on crude oil data are discussed with the help of graphs for the predictions for next 50 days, actual and predicted plots, model performance plot, and validation performance plot for all the methods. Compared to the previous published work, the accuracy of the problem has been verified by comparing it to the quantitative outputs exhibited in this, and the agreement between the results is excellent, which has led to confidence in the quantitative results projected in this study.. KEYWORDS :Crude oil price, ARIMA model, Time series forecasting, Machine learning techniques, Error metrics.