Forecasting retail fuel demand in Chinese gasoline stations: a structural (double) damped trend approach

Abstract

Forecasting retail fuel demand represents a crucial task for petrol companies. Indeed, the accurate prediction of demand allows improving the management of gasoline stations as well as the whole oil supply chain process. This paper provides a time series forecasting model, defined as double damped trend model, that allows efficiently forecasting the daily retail fuel demand. We propose a flexible state-space approach that encompasses several exponential smoothing alternatives. This model was first considered for the case when a single source of error drives the dynamics of the process. However, little attention was given to the case where multiple sources of errors drive its dynamics, the so-called structural approach. In this paper we focus on this case by providing closed-form results that allows simplify its likelihood estimation as well as the construction of prediction intervals. Moreover, using data for more than 400 gasoline stations in China, we show that our approach outperforms standard benchmarks in predicting both the amount of customer’s demand and prediction intervals, for different types of fuels. These results make our approach appealing for petrol companies.