Modeling and Predicting Indonesia’s Inflation Using the ARIMA Model
Abstract
Inflation is one of the most important macroeconomic indicators used to evaluate the stability and performance of a country's economy. This study aims to model and predict Indonesia’s monthly inflation rate using the Autoregressive Integrated Moving Average (ARIMA) approach. The dataset consists of monthly inflation observations from January 2010 to December 2025 obtained from Bank Indonesia. The analysis begins with testing the stationarity of the series using the Augmented Dickey–Fuller (ADF) test, followed by model identification through the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots. Several candidate models are estimated, including ARIMA (0,1,1), ARIMA (1,1,0), and ARIMA (1,1,1). Model comparison based on the Akaike Information Criterion (AIC) indicates that the ARIMA (0,1,1) model provides the lowest AIC value and is therefore selected as the most appropriate model. The forecasting results suggest that Indonesia’s inflation rate is expected to remain relatively stable at around 3.63% over the next six periods. However, the prediction intervals become wider as the forecasting horizon increases, reflecting growing uncertainty in longer-term predictions.
References
Bhandari, A., & Frankel, J. (2023). Inflation forecasting and the role of time series models. Journal of International Money and Finance, 130. https://doi.org/https://doi.org/10.1016/j.jimonfin.2022.102761
Bhowmik, R., & Wang, S. (2020). Stock market volatility and return analysis: A systematic literature review. Entropy, 22(5), 522. https://doi.org/https://doi.org/10.3390/e22050522
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2016). Time series analysis: Forecasting and control (5th ed.). Wiley.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2021). Time series analysis: Forecasting and control (6th ed.). Wiley.
Burnham, K. P., & Anderson, D. R. (2020). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Springer.
Chatfield, C., & Xing, H. (2019). The analysis of time series: An introduction with R (7th ed.). CRC Press.
Claeskens, G., & Hjort, N. L. (2019). Model selection and model averaging. Cambridge University Press.
Enders, W. (2022). Applied econometric time series (5th ed.). Wiley.
Etuk, E. H., Moffat, I. U., & Chukwu, B. I. (2021). Modeling Nigerian inflation rates using seasonal ARIMA models. International Journal of Statistics and Applications, 11(2), 39–46. https://www.eajournals.org/wp-content/uploads/Modeling-Inflation-Rates-in-Nigeria-Box-Jenkins-Approach.pdf
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). Statistical and machine learning forecasting methods: Concerns and ways forward. PLOS ONE, 17(3). https://doi.org/10.1371/journal.pone.0264549
Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2021). Introduction to time series analysis and forecasting (3rd ed.). Wiley.
Nyoni, T., & Bonga, W. G. (2021). Modeling and forecasting inflation using ARIMA models. Dynamic Research Journals’ Journal of Economics and Finance, 6(1), 1–10. https://ideas.repec.org/p/pra/mprapa/92458.html
Saini, S., & Sinha, A. (2022). Forecasting inflation using ARIMA and machine learning models: Evidence from emerging economies. Journal of Economic Studies, 49(6), 1105–1122. https://www.researchgate.net/publication/394049793_A_Comparative_Study_of_Machine_Learning_and_ARIMA_Models_for_Inflation_Forecasting_in_Nigeria
Shumway, R. H., & Stoffer, D. S. (2021a). Time series analysis and its applications: With R examples (4th ed.). Springer.
Shumway, R. H., & Stoffer, D. S. (2021b). Time series analysis and its applications (4th ed). Springer.
Tsay, R. S. (2020). Analysis of financial time series (3rd ed.). Wiley.
Wei, W. W. S. (2019). Time series analysis: Univariate and multivariate methods (2nd ed.). Pearson.
