FORECASTING INFLATION IN INDONESIA USING THE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE METHOD

Abstract

Indonesia faces significant economic challenges, particularly inflation, which affects the economic, social, and cultural sectors. High inflation can exacerbate poverty, alter consumption patterns, and contribute to social injustice, whereas low inflation can enhance national income and stimulate economic activities. Given its fluctuating nature, inflation in Indonesia requires accurate forecasting to inform policy-making and economic decisions. This study aims to forecast inflation in Indonesia for the next eight months using the Autoregressive Integrated Moving Average (ARIMA) method. Monthly inflation data from January 2020 to April 2024 obtained from Bank Indonesia were analyzed. The ARIMA model, suitable for short-term forecasting, was selected due to its ability to handle data trends, non-stationarity, and noise filtering. The Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests to ensure stationarity. Initial ADF tests showed the presence of a unit root in the original data and the first differencing data, but data became stationary after the second differencing. The KPSS test confirmed a unit root in the original data and trend stationarity after the second and third differencing. Ordinary Least Squares (OLS) regression on the original data revealed a significant time trend, indicating deterministic trends. The optimal model identified was ARIMA(0,2,1) with AIC=51.81, as it met the criteria for normality, independence, and zero mean of residuals. This model effectively forecasts inflation from May to December 2024, which showed an increase with inflation values ​​of 3.02, 3.05, 3.07, 3.10, 3.12, 3.14, 3.17, and 3.19.