Modeling inflation in indonesia using the adf stationarity test, garch, and random forest regression based on monthly consumer price index data from 2014 to 2024
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time series,
garch,
random forest,
Indonesia
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Abstract
Inflation is one of the key macroeconomic indicators that reflects price stability and overall economic conditions of a country. This study aims to examine the characteristics, volatility, and forecasting of monthly inflation in Indonesia over the period 2014–2024 by employing descriptive statistical analysis, time series approaches, and machine learning methods. The descriptive results indicate that Indonesia’s inflation has an average rate of 1.71% with a standard deviation of 1.32, a minimum value of 0.04%, and a maximum value of 8.36%. The inflation distribution is asymmetric, as evidenced by a positive skewness of 1.88 and a kurtosis value of 5.15, suggesting the presence of extreme inflation spikes during certain observation periods. Time series analysis reveals that the inflation data do not fully satisfy the stationarity assumption at the level form, thus requiring differencing prior to further modeling. Volatility estimation using the GARCH model indicates the existence of inflation volatility persistence. Furthermore, forecasting results using the Random Forest method demonstrate that the model is able to closely track actual inflation patterns under normal conditions, although predictive accuracy declines during periods of heightened volatility. Overall, this integrated approach provides a more comprehensive understanding of the dynamics of inflation in Indonesia.
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