Rupiah exchange rate prediction against the US Dollar using a deep neural network with a multi-output sliding window...

The Rupiah USD exchange rate is a critical macroeconomic indicator in Indonesia, yet its prediction remains challenging due to volatility, nonlinear dynamics, and seasonal fluctuations. This study proposes a deep learning-based forecasting approach using a multi-output sliding window framework to simultaneously predict buying and selling rates. Daily historical data from 2015 to 2025 were normalized and enhanced with sine-cosine time encodings to capture weekly cyclical patterns. Three neural network architectures, Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were evaluated, with Vector Autoregression (VAR) serving as a statistical benchmark. Model performance was assessed using MAPE, RMSE, and R². The GRU model demonstrated superior accuracy and stability across volatile periods. A secondary evaluation with a 70:30 train test split confirmed its robustness, achieving an RMSE of 64.57, MAPE of 0.0031, and R² of 0.9875. It also produced reliable short-term forecasts up to seven days ahead, underscoring its practical applicability for financial decision making.•The method enables simultaneous prediction of buying and selling rates using a multi-output sliding window.•GRU showed the highest accuracy compared to LSTM, MLP, and VAR, with consistent performance across different splits.•The approach supports short-term economic forecasting and decision-making in volatile currency environments.