Abstract: Forecasting palm oil price volatility is challenging due to nonlinear dynamics and temporal dependencies inherent in agricultural commodity markets. This study proposes a hybrid deep learning framework combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks to enhance univariate palm oil price forecasting. The key contribution lies in integrating LSTM’s long-term memory capability with GRU’s computational efficiency to capture multi-scale temporal patterns within a unified architecture..........
Key Word: Palm oil price forecasting, LSTM, GRU, hybrid deep learning, time-series prediction, commodity markets, artificial intelligence.
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