Comparative Analysis of Statistical, Machine Learning, and Deep Learning Models for USD/IDR Prediction Using Macroeconomic Indicators and Explainable Artificial Intelligence

Abstract

The USD/IDR exchange rate is a key daily barometer of Indonesia's economic health. Accurate forecasting is vital for trade, inflation, and monetary stability. However, its volatile and nonlinear dynamics pose challenges. While research has applied statistical models, machine learning, and deep learning, few studies offer a comprehensive comparison integrating predictive accuracy with model interpretability, particularly using banking stock prices as exogenous predictors. This study addresses this gap by developing and evaluating eight forecasting approaches—a naive random-walk baseline, two statistical models (ARIMA, SARIMAX), three tree-based ensembles (Random Forest, XGBoost, LightGBM), and two recurrent neural networks (LSTM, GRU) using daily data from January 2015 to July 2026 (3,005 observations). Five major banking stocks (BBCA, BBRI, BMRI, BBNI, BDMN) are included as one-day-lagged exogenous features. Models are assessed via hold-out testing and five-fold walk-forward cross-validation using RMSE, MAE, MAPE, and R². Contrary to expectations, the naive random-walk consistently achieves the lowest error (RMSE=115.24, MAE=63.80, MAPE=0.39%) and the most stable performance, with LSTM as the best-performing complex model (RMSE=269.59, R²=0.785). Diebold-Mariano tests confirm statistical significance (p<0.001). To enhance transparency, SHAP-based Explainable AI is applied to Random Forest, revealing that the lagged USD/IDR value overwhelmingly dominates predictions (mean |SHAP|=1,509.01), while banking stock contributions are negligible. These findings also empirically confirm the well-known Meese-Rogoff puzzle and weak-form market efficiency for USD/IDR, clearly proving that simple baselines remain formidable benchmarks for short-horizon forecasts. This study ultimately underscores the critical importance of combining rigorous benchmarking with XAI to deliver accurate and interpretable predictions for economic policymakers and financial practitioners.