Forecasting Gasoline Demand in Indonesia Using Time Series

Sylvia Mardiana, Ferdinand Saragih, Martani Huseini

Abstract


Fuel is an essential commodity in both the economy and society. Indonesian fuel demand continues to increase annually, whereas fuel production has decreased. Gasoline accounts for more than 50% of fuel consumption for transportation. A reliable gasoline product demand forecast is required to plan the gasoline supply. The objective of this study is to forecast the demand for total gasoline and its three components, which are gasoline 88, gasoline 90, and gasoline 92. This study compared the Holt–Winters additive model and autoregressive integrated moving average for the time-series data for the 2017–2019 period. Because the Holt–Winters additive model generates more accurate results, it was applied to predict the total demand for gasoline during 2020–2022. The results of the combination of the Holt–Winters model and a neural network to forecast gasoline 92 demand had lower errors than the individual Holt–Winters method. The forecast results show that total gasoline demand is forecasted to increase, but the components indicate a different trend. Gasoline 92 and gasoline 88 decreased, but gasoline 90 increased.

Keywords: Forecasting, time series, gasoline demand, Holt-Winters, Neural Network

JEL Classifications: Q4, Q47

DOI: https://doi.org/10.32479/ijeep.9982


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