Forecasting energy data with a time lag into the future and Google trends

This paper presents a new idea for a forecasting approach which seeks to exploit the information contained within US EIA energy forecasts and related Google trends data for generating a new and improved forecast. The novel forecasting approach can be exploited by using a multivariate system which can consider data with different series lengths and a time lag into the future. Using real historical data, an official forecast for the same variable, and Google Trends search data, we illustrate the possibility of generating a comparatively more accurate forecast for an energy-related variable. The accuracy of the newly generated forecasts are evaluated by comparing with the actual observations and the official forecast itself. We find that the novel forecasting idea cangenerate promising results which call for further in-depth research into developing and improving this multivariate forecasting approach.