# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 2 Literature Review  
The following paragraphs will present three aspects of the price volatility pattern in small agricultural products in China, how this combined model determines its base model, and the weights between the base models. First of all, in the field of price forecasting in small agricultural products, previous researchers have conducted many studies in the field of price fluctuation law, which helped us to determine the fluctuation law of scallion price and then determine the forecasting model. For example, a researcher [41] conducted an in-depth analysis of the characteristics of garlic price fluctuations, revealing the seasonality and cyclicality in price fluctuations, and these fluctuation characteristics may also exist in scallion prices. One scholar [42] has studied combined model prediction for ginger prices by analyzing the factors affecting ginger price, studying the fluctuation law in ginger price, and constructing a ginger price prediction model based on a Prophet-SVM combined model to predict the trend of ginger price, providing an important reference for us to understand the price fluctuation factors and predict the price of green scallion. Other research results show [43] that all small agricultural products have significant seasonality, cyclicality, and trends. The results of various studies show that the price fluctuations of small agricultural products are regular and can be predicted by modeling. Secondly, the techniques of decomposition, reconstruction, and single model forecasting used in this study have been used in previous cases of agricultural price forecasting. In the field of traditional forecasting, the ARIMA model is commonly used to forecast the price of agricultural products, and a study [44] used ARIMA as one of the base models in a combination model, which is capable of interpreting and forecasting the data in a simple and flexible linear manner. This provides empirical support for the use of similar methods to deal with the scallion price data in this study. In machine learning methods, LSTM and GRU models, which are intelligent models, are also often applied in the field of agricultural price prediction [45]. For example, there has been a study on predicting Chinese agricultural futures prices by constructing a VMD-SGMD-LSTM emerging hybrid model [46], while LSTM is essentially an improved RNN, which can improve the dilemma of the standard RNN where the gradient disappears or explodes during the training process, and a study has shown that it has a better learning and prediction ability for nonlinear time series. Another study predicts the spot price of soybean in China by constructing a CNN-GRU-Attention model [47], which demonstrates the adaptive learning ability of the GRU model, allowing it to cope well with the difficult problem of dynamic changes in commodity prices and reduce the long-term dependence problem well through its gating mechanism. As a more computationally efficient recurrent neural network, it is a good choice for predicting the price of such small agricultural commodities. In summary, the base models selected for the combined model in this study include the classical model ARIMA in the field of traditional time series forecasting, and the two machine learning models LSTM and GRUs, which are quite effective in the field of small agricultural products. Finally, the selection of an appropriate optimization algorithm for determining the weights of the combination model is often the key to obtaining the best combination forecasting results. It has been clearly shown that the serial hybrid optimization algorithm with PSO followed by the GA excels in optimization problems with multiple peak functions, like determining the optimal weights of the combined model [48]. One study implemented the optimization of network model parameters through PSO-GA [49], which helped to avoid overfitting, improve the generalization ability of the model with unknown data, and use the optimized network to forecast the future electricity consumption in Wuhan.