This paper is structured as follows: “Related work” section provides a brief overview of the existing literature on forecasting agricultural futures prices using time series analysis. “Methodology” section introduces the foundational theoretical concepts relevant to our study. “Experiment” section details the proposed approach for predicting agricultural commodity prices. In “Results” section , we present the experimental setup and results, followed by the conclusions in “Discussion” section.
In the field of futures price prediction, existing models can generally be categorized into three types: statistical models, artificial intelligence models, and hybrid models. Statistical models are commonly employed to analyze and predict price fluctuations in futures markets. For example, Evans’ study demonstrated that U.S. economic news announcements have a significant correlation with intra-day price volatility in the futures market. Stoll et al. found that the transmission of trading signals is time-sensitive, with 5-minute high-frequency data outperforming 10-minute data in predicting price volatility. Brooks used the ARCH model to study price volatility in futures markets, revealing that volatility follows a non-homogeneous distribution. Bunnag applied the GARCH model to predict crude oil futures prices and calculate the optimal oil investment portfolio weights. Huang et al. utilized GARCH and EGARCH models to estimate and forecast price volatility of four agricultural commodity futures, confirming the effectiveness of GARCH models in this domain. Despite the effectiveness of traditional statistical models like GARCH, they often rely on strict statistical assumptions, which may not hold in rapidly changing futures markets. Futures price time series often exhibit nonlinear and non-stationary characteristics, and traditional statistical models have limitations when dealing with these complex features, thus affecting predictive accuracy.
In contrast, machine learning -based models, particularly those utilizing decomposition and reconstruction frameworks, have garnered increasing attention in recent years. For instance, Wen et al. highlighted that a hybrid model combining Singular Spectrum Analysis and Support Vector Machines significantly outperforms both SVM-only models and other hybrid models in financial price prediction. Zhu et al. demonstrated the superiority of a combination of Variational Mode Decomposition and Bidirectional Gated Recurrent Unit in forecasting rubber futures prices, showing marked improvement over standalone BiGRU models. Liu et al. combined VMD with Artificial Neural Networks for predicting energy and metal prices, with experimental results showing that VMD-based models significantly outperform traditional methods. Wang et al. proposed a hybrid neural network model based on Empirical Wavelet Transform for oil price forecasting, with results indicating that EWT effectively extracts both the overall trend and local volatility features of price movements, thereby improving forecasting accuracy. However, while decomposition methods significantly enhance predictive performance, using all decomposed components in the prediction model can lead to increased computational complexity. Moreover, Yu et al. pointed out that during the final ensemble prediction process, errors from all components may accumulate and negatively impact the final results. To address these challenges, recent research has focused on decomposition-reconstruction ensemble methods, which optimize prediction accuracy by removing components with minimal contribution (e.g., those contributing less than 2%.
Despite the success of these machine learning approaches, two key challenges remain: (i) most existing methods rely on manual feature selection, which reduces flexibility and heavily depends on domain knowledge. In some cases, local dynamics in time series contain critical information (e.g., early changes in medical signals or abnormal weather patterns), which necessitates an automated feature extraction process; (ii) current futures price prediction literature has not achieved full automation, and it is challenging to effectively test model performance in large-scale data settings. As a result, recent research has increasingly turned to deep learning models.
For example, Li and Zheng proposed a framework combining a value-based Deep Q Network with a key behavioral model for profit, which effectively addresses noise and non-stationarity in financial data. This model incorporates Stacked Denoising Autoencoders and Long Short-Term Memory networks, achieving stable risk-adjusted returns in stock index futures trading. Jeong and Kim improved the DQN model by integrating Deep Neural Networks to predict trading volumes. Wu et al. introduced the combination of Gated Recurrent Units and deep reinforcement learning, proposing GDQN and GDPG futures trading strategies, which performed well under various market conditions, especially in volatile markets. Gao et al. optimized DQN training by incorporating a Prioritized Experience Replay (PER) mechanism, showing that it outperformed ten traditional strategies, including the buy-and-hold strategy (B&H).
Building on the latest advancements in deep learning, Jiang et al. proposed a novel framework for predicting abnormal price fluctuations in agricultural futures, based on time-series images. The approach first transforms one-dimensional time series data into two-dimensional images and then applies IFFA for data preprocessing, sorting 15-day time series data numerically. To address small sample overfitting, transfer learning is employed, and a CNN model selector is trained to compute prediction errors and compare them with existing methods. This framework not only improves the prediction accuracy for agricultural futures prices but also provides effective early-warning support, enhancing the capacity for agricultural price risk management.
The main contributions of this study are as follows:
