Forecasting of Indonesian Crude Prices using ARIMA and Hybrid TSR-ARIMA - Etik Zukhronah; Winita Sulandari; Sri Subanti; Isnandar Slamet; Sugiyanto Sugiyanto; Irwan Susanto (LPPM Institut Teknologi Sepuluh Nopember, 2024)
Forecasting of Indonesian crude prices (ICP) is crucial for the government and policymakers. It helps them develop appropriate economic policies, budget allocations, and energy strategies. Forecasting methods that can be used are Time Series Regression (TSR) and Autoregressive Integrated Moving Average (ARIMA). This study aims to forecast ICP using ARIMA and hybrid TSR-ARIMA models. The data used in this study is the ICP per month, from January 2017 to November 2022. The data is divided into two groups, the data from January 2017 to December 2020 is used as training data, and the data from January 2021 to November 2022 is used as testing data. The MAPE values for the testing data of the TSR-ARIMA(2,1,0) and ARIMA(2,1,0) models are 8.24% and 17.37% respectively. Based on this, it can be concluded that the TSR-ARIMA(2,1,0) model is better than the ARIMA(2,1,0) model for forecasting ICP.