explore scientific methods to predict the market prices of them. This could help relevant practitioners make correct judgments and scientific decisions to avoid market risks and promote the healthy development of the small-scale agricultural product industry. This paper takes garlic, the most representative small-scale agricultural product of China, as an example, to explore a price-prediction method for small-scale agricultural products. The prices of small-scale agricultural products such as garlic are characterized by high volatility, strong nonlinearity, and nonstationarity, and also exhibit some seasonal and cyclical fluctuations [4]. These pose a great challenge to the garlic price prediction task. The prices are influenced by a variety of factors. However, some influencing factors are difficult to measure quantitatively. It is difficult to put all factors in when building a forecasting model. Since these factors are concentrated in the historical price series, it is beneficial to fully explore the volatility characteristics of historical prices to improve the accuracy of the forecasting model. The key to improving prediction performance is to fully exploit the complex regular information in the price series. Some classical econometric model studies in the past have helped to achieve short-term forecasts of agricultural prices, such as ARIMA, GARCH, and other linear models [5; 6; 7; 8]. However, such models ignore the non-stationary and non-linear characteristics of agricultural price series in modeling and have difficulty capturing the long-term dependence in the series. Moreover, it has been pointed out in the literature that when garlic prices suddenly and dramatically fluctuate, there is still a large gap between the predicted results of the econometric model and the actual values [9]. Machine-learning methods with flexible operational design and powerful self-learning capabilities have been introduced to overcome the shortcomings of econometric models in price forecasting tasks. Such models were found to be effective in learning nonlinear features in time series and improving forecasting performance. They have been shown to be robust in various nonlinear modeling tasks, such as classification and feature extraction [10; 11; 12]. Among the machine-learning models, artificial neural networks (ANNs) are powerful for predicting nonlinear data [13]. However, due to its shallow structure, the ANN cannot store network states and can hardly deal with the long-term dependence of time series. Based on this, deep-learning methods have been developed and applied in the field of agricultural product price prediction [14]. Mainstream deep-learning models include the CNN, RNN, and other models [15]. For the time series prediction problem, the recurrent neural network (RNN) is a common and effective method. Still, it often suffers from gradient disappearance or gradient explosion when dealing with long series. Long short-term memory (LSTM) and gated recurrent unit (GRU), which are the variants of the RNN, are starting to emerge. LSTM, proposed by [PERSON]. [16], and GRU, proposed by [PERSON] [17], address the structural defects of the RNN and can effectively deal with the long short-term dependence and non-linear characteristics of sequences. In addition, a great deal of research has proved that the linear and nonlinear patterns of price series cannot be captured simultaneously by a single model alone, and agricultural products prices are no exception [18; 19; 20]. Therefore, combined model methods have been introduced to the agricultural products price prediction task, which combine the advantages of different prediction models by integrating them to capture the linear and nonlinear patterns. Combined models can provide enhanced interpretability while improving the accuracy of price predictions of agricultural products. Inspired by the above studies, for more accurate prediction of garlic prices, we combined the advantages of deep-learning models and econometric models and propose combined prediction models.