Forecasting Aluminum Futures Prices: A Comparative Evaluation of Classical, Deep Learning and Transformer-based Models

Abstract

Forecasting commodity prices remains a challenging task due to market volatility, structural breaks, and changing economic conditions. This study evaluates the forecasting performance of classical econometric, deep learning, convolutional, and Transformer-based models for aluminum futures prices. Daily aluminum futures data are analysed using 11 forecasting approaches. Forecasting experiments are conducted for two distinct evaluation periods representing the years 2022 and 2025 to assess the robustness of model performance under different market environments. The empirical results reveal substantial differences in forecasting performance across model families. Recurrent neural network architectures, particularly GRU and RNN, consistently achieve the lowest forecasting errors across most horizons. The classical ARIMA model remains highly competitive despite its relative simplicity. In contrast, Transformer-based models generally fail to outperform simpler alternatives and frequently produce higher forecast errors. Statistical comparisons based on Diebold–Mariano tests and Model Confidence Set procedures indicate that performance differences among the best-performing models are often limited, suggesting that increased model complexity does not necessarily translate into superior predictive accuracy. Overall, the findings highlight the importance of empirical model evaluation and demonstrate that parsimonious forecasting approaches can remain effective competitors to substantially more complex machine learning architectures in aluminum futures price forecasting.