Multivariate Forecasting of Crude Oil Spot Prices using Neural Networks

Abstract--Crude oil is a major component in most advanced economies of the world. Accurately predicting and understanding thebehavior of crude oil prices is important for economists, analysts, forecasters, and traders, to name a few. The price of crude oil has declined in the past decade and is seeing a phase of stability; but will this stability last? This work is an empirical study on how multivariate analysis may be employed to predict crude oil spot prices using neural networks. The concept of using neural networks showed promising potential. A very simple neural network model was able to perform on par with ARIMA models - the state-of-the-art model in time-series forecasting. Advanced neural network models using larger datasets may be used in the future to extend this proofof-concept toa full scale framework. I. INTRODUCTION Crude oil spot prices saw a tremendous uptick in the first decade of the 21 Since 2014, crude oil prices have fallen and may have stabilized now. However, there has always been a constant interest in accurately predicting crude oil prices; given that crude oil drives a major portion of the economy. Economists, scientists, data analysts, and traders are all interested in models that give them the best accuracy.