Comparison of LSTM and ARIMA Methods in Predicting the Inflation Rate in Manado City - Skolastika Kadang; Vivi Peggie Rantung (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
Forecasting city-level inflation is challenging due to seasonal patterns, nonlinear dynamics, and limited exogenous variables, while short-term accuracy is required for timely policy responses. This study focuses on monthly inflation in Manado City over the period 2010–2024, explicitly accounting for the role of the Consumer Price Index (CPI). We compare a seasonal SARIMA baseline with a multivariate LSTM model that jointly ingests inflation and CPI series. The contributions of this work are an end-to-end, reproducible forecasting pipeline and an evidence-based comparison that identifies the conditions under which a feature-rich nonlinear model is preferable. The methodology includes aligning and preprocessing monthly series, conducting stationarity tests, selecting SARIMA specifications via information criteria and residual diagnostics, and training a 12-month window LSTM (Adam optimizer, MSE loss) with internal validation. The results show that the LSTM yields lower errors on the test horizon (RMSE 0.497; MAE 0.398) than the SARIMA (1,1,1)×(1,1,1,12) model (RMSE 0.661; MAE 0.486), with a smoother 12-month-ahead forecast path under a constant-CPI scenario; visual findings are consistent with the metrics, and a Diebold–Mariano test can be used to assess the significance of the difference. In conclusion, although SARIMA remains a strong and interpretable baseline, the multivariate LSTM delivers a practically meaningful gain in short-term accuracy when the inflation–CPI interaction is nonlinear, making it relevant for regional policy planning.