Indonesian Oil and Gas Export Data Forecasting Using Autoregressive Integrated Moving Average and Exponential Triple Smoothing Methods - Kurniawan, Deni; Afrianto, Irawan (International Journal of Research and Applied Technology (INJURATECH), 2025)
This study analysed historical data movement patterns and forecasted the value of oil and gas exports for the next 12 periods. The data obtained was secondary data in the form of monthly time series for the period (2021–2025). A time series is a series of observations of a variable that occur in relation to the time of its occurrence, while time series data is data collected over a specific time period. The analysis method used was the Box-Jenkins method or Autoregressive Integrated Moving Average with the help of R software. The model obtained through the stages of model identification, parameter estimation, and diagnostic testing was then used to project the data. The best model was selected based on the smallest Akaike Information Criterion (AIC) value and valid parameter significance tests. The forecast results for 2026 show a trend that tends to be constant or flat following the latest data level, with a confidence interval that widens as the time period increases. This indicates that the value of oil and gas exports is predicted to be stable but carries a high risk of uncertainty in the future, thus requiring adaptive policy anticipation to address market volatility.