## 1 Introduction  
Agricultural product prices, as an important component of the agricultural market in the modern economic system, are closely related to the vital interests of producers and operators, and even the quality of life of consumers [1]. A decrease in the prices of agricultural products in the wholesale market leads to a reduction in earnings for producers and traders, while an increase in these prices results in a higher cost of living for consumers. Moreover, frequent and significant price volatility can disrupt the stability of the market and impede the sustainable growth of the agricultural industry's supply chain, ultimately harming the interests of producers, operators, and consumers [2, 3]. Therefore,it is essential to carry out precise predictions regarding the trends of agricultural product price fluctuations. Agricultural product prices in wholesale markets are affected by various external factors, including production costs, changes in supply and demand, climatic conditions, and government policies and interventions. The prices fluctuate rapidly and extensively, presenting nonlinear and non-smooth characteristics; therefore, accurately predicting agricultural prices is challenging. There are numerous models for the forecasting of agricultural commodity prices. Based on the number of models utilized, forecasting models can be classified as either single or combined models [4]. Models can be divided into two main types: statistical models and machine learning models. Statistical price forecasting models mainly comprise the trend extrapolation method, exponential smoothing method, regression forecasting method, and gray forecasting model [5; 6]. While statistical models are characterized by the strong interpretability of the results, they have a limited capacity to handle complex nonlinear time-series data [6]. Due to their excellent self-learning and self-adaptive capabilities, machine learning models are widely applied in forecasting [7], and mainly include the backpropagation neural network (BPNN), general regression neural network (GRNN), support vector regression (SVR) [8], and extreme learning machine (ELM). However, these models are sometimes characterized by difficult parameter selection [9; 10; 11; 12; 13]. In order to enhance the predictive accuracy of individual models and to leverage their strengths while mitigating their weaknesses, the idea of composite forecasting was first proposed, which involves blending various individual forecasting techniques through a weighted average approach [14]. Ensemble forecasting models enhance the variety within predictive systems, allowing for the comprehensive retrieval of valuable insights from individual model forecasts and bolstering the stability of outcomes in unforeseen circumstances [15]. These ensemble models are broadly classified into three types. The initial type encompasses models that utilize data decomposition techniques [4]. Within the realm of intricate time-series forecasting, the strategy of decomposing and then integrating data is viewed as a potent approach to enhance predictive precision. The essence of this strategy lies in breaking down intricate time series into simpler, more manageable segments, which streamlines the forecasting model development process [3; 16]. The second type of ensemble model is predicated on the assignment of weights, with the goal of allocating optimal weights to minimize the combined model's predictive error, given that the weights are non-negative and normalized [17; 18]. The primary methods for combining models in this category involve weighted averaging algorithms and techniques that minimize the aggregate sum of squared errors. The third type of ensemble forecasting model is founded on error correction principles. Given that errors in agricultural price forecasting are inevitable, employing an error correction strategy can mitigate these errors and, in turn, enhance the precision of the forecasted outcomes.