Comparative Analysis of ARIMA and Machine Learning Models for Forecasting Inflation in Cambodia

Abstract

This research tests the predictive power of selected classical time-series and machine learning models for Cambodia’s LNCPI. Monthly observations from January 2008 to April 2026 were split sequentially into an 80% training set and a 20% test set. An automatic seasonal autoregressive integrated moving-average model, ARIMA(3,1,1)(1,0,0)[12] with drift was used as the benchmark and compared to Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR). For forecasts within the test period, we recursively used lagged values of LNCPI and a deterministic time trend, as well as indicators for month to capture any seasonality. Root Mean Squared Error (RMSE), with the raw values characterizing forecast accuracy like Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The ARIMA model outperformed the others on all three out-of-sample accuracy metrics, producing an RMSE of 0.0170, MAE of 0.0145, MAPE of 0.2726%. Random Forest, XGBoost, and SVR yielded RMSEs of 0.0398, 0.0369 and 0.0474 respectively. Variable-importance results showed that most predictive information rested in the time trend and the first two LNCPI lags, with monthly seasonal indicators contributing little. As the forecast horizon increased, machine-learning forecasts began to flatten out and increasingly under-predict LNCPI. The results show that using a parsimonious seasonal ARIMA specification based on a relatively small univariate dataset has more accurate forecasts than the selected machine-learning models.