Hybrid Modeling of Inflation Dynamics: Integrating ARIMA with Markov Chains

Nurwahidah Nurwahidah, Syaharuddin Syaharuddin, Abdillah Abdillah, Vera Mandaillina, Sirajuddin Sirajuddin, Mahsup Mahsup

Abstract


Abstract: Inflation is a macroeconomic indicator that plays a strategic role in reflecting the economic stability of a country. Therefore, accurate inflation forecasting is important to support decision-making in fiscal and monetary policy. This study aims to develop a hybrid model that integrates the Autoregressive Integrated Moving Average (ARIMA) and Markov Chain methods in order to improve the accuracy of inflation predictions. The ARIMA model is used to capture linear patterns in inflation data, while the Markov Chain is used to model probabilistic transitions between stochastic inflation states. The data used is secondary monthly inflation data for the period 2015–2024 obtained from the Central Statistics Agency (BPS). Model performance evaluation is carried out using three main metrics, namely Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) of 74.95%. The results show that the ARIMA–Markov hybrid model is able to improve prediction accuracy compared to a single approach and is more adaptive in capturing the complex dynamics of inflation. These findings are expected to contribute to the development of more responsive and accurate economic forecasting methods.

Keywords


Inflation, Forecasting, ARIMA, Markov Chain, Hybrid Model, Time Series Data.

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