Comparative evaluation of deep learning models for dried corn price prediction in east java - Antika Zahrotul Kamalia; Choiriyatun Nisa Latansa; Zaenur Rozikin; Hemdani Rahendra Herlianto; Shiza Hassan (Journal of Soft Computing Exploration, 2026)
Forecasting dry shelled corn prices was important for supporting decision-making by farmers, traders, feed industries, and local governments. This study comparatively evaluated several deep learning models, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network 1D (CNN1D), Temporal Convolutional Network (TCN), and Transformer, for predicting dry shelled corn prices in East Java. Classical benchmark models, namely naïve, drift, and simple exponential smoothing (SES), were also incorporated into the experimental design. Using daily price data from 2020 to 2024, a 30-day lookback window, and multivariate features derived from price movements, calendar variables, and rolling statistics, model performance was assessed using MAE, RMSE, MAPE, sMAPE, and . The results showed that the naïve baseline achieved the best overall performance on the 2024 test set, while TCN was the strongest among the evaluated deep learning models. TCN obtained RMSE of 176.95 and of 0.6895, whereas the naïve baseline achieved RMSE of 20.06 and of 0.9960. Overall, all deep learning models were outperformed by the naïve persistence benchmark, indicating that greater model complexity did not automatically improve forecasting accuracy on this highly persistent price series.