Times series analysis on crude oil price

Abstract

This paper mainly investigates monthly crude oil price prediction leveraging some traditional time series models and machine learning models. We combined variables suggested from past literature with models suggested from other literature to figure out an approach to improve model prediction accuracy on monthly crude oil price. We first did preliminary variable selections to choose the variables that are most likely related to the crude oil price. Then we established different time series models including a machine learning model on the selected variables. Our approach of modeling and predicting monthly crude oil prices performs well, with slight improvement compared with literature in terms of prediction accuracy. The OLS model plays an important role in variable selection, which improves the model’s performance. LSTM model has the best overall performance, especially did a good job in forecasting oil price during pandemic without overfitting.