Ensemble Bagging Stacked LSTM for Forecasting Inflation in Indonesia Using USD/IDR Exchange Rate

Abstract

Inflation forecasting remains a complex problem due to nonlinear dynamics and interactions among macroeconomic variables, particularly in emerging economies such as Indonesia. Previous studies using deep learning models, including Long Short-Term Memory (LSTM), have shown promising results but often suffer from high variance and limited robustness, especially when temporal dependencies are not properly preserved. This study aims to develop a more stable and accurate forecasting model by integrating Bagging with a Stacked LSTM architecture using the Moving Block Bootstrap (MBB) method. The proposed model utilizes multivariate time series data consisting of inflation, exchange rate (USD/IDR), BI interest rate, and money supply, with preprocessing techniques including Z-score normalization and sliding window transformation. Experimental results show that the model achieves an RMSE of 0.4273 and MAE of 0.3048, indicating good predictive performance. Compared to baseline models such as ARIMA and single LSTM, the proposed approach provides more stable and consistent forecasting results. The model is also able to generate reliable predictions for the next 12 periods, demonstrating its ability to capture temporal patterns effectively. These findings suggest that the integration of Bagging, Stacked LSTM, and MBB improves model robustness and forecasting accuracy. The proposed approach can support data-driven decision-making in economic policy, although further research is needed to incorporate additional variables and explore more advanced forecasting architectures.