Crude oil price forecasting with machine learning and Google search data: An accuracy comparison of single-model versus multiple-model.

Recent research has shown that introducing online data can significantly improve forecasting ability. This study considers several popular single-model machine learning methods and a stacking multiple-model ensemble learning strategy. These are used with online data from Google Trends to forecast crude oil prices. The study first selects dozens of alternative Google Trends, which may capture crude oil price fluctuations. A co-integration test and Granger causality analysis are used to investigate the effect of Google Trends on crude oil prices. Then, the multiple-model methods are compared with several popular single-model machine learning methods that are used to forecast crude oil prices. These methods are used with Google Trends that have a significant relationship with the crude oil price. Experimental results indicate that introducing Google Trends can improve the forecasting performance; multiple-model methods also outperform several popular single-model machine learning methods in terms of prediction accuracy. • We develop an effective framework of oil price forecasting with Google trends. • The framework includes skillful indicators selection and forecasting methods. • Practical study finds Google trends that significantly impact on crude oil price. • The proposed multiple-model methods show obvious advantages in practical study. [ABSTRACT FROM AUTHOR]