Multivariate Forecasting of Crude Oil Spot Prices using Neural Networks
This work addresses the problem of predicting crude oil prices for economists, analysts, and traders, but it is incremental as it builds on existing neural network methods without major innovations.
The study tackled crude oil spot price forecasting by using neural networks for multivariate analysis, finding that a simple neural network model performed comparably to ARIMA models, a state-of-the-art approach in time-series forecasting.
Crude oil is a major component in most advanced economies of the world. Accurately predicting and understanding the behavior 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 proof-of-concept to a full scale framework.