## 2 Related Work
### Agricultural Price-Prediction Methods  
Common traditional econometric methods include autoregressive integrated moving average (ARIMA) models and generalized autoregressive conditional heteroskedasticity (GARCH) models. The advantages of traditional econometric models lie mainly in the theoretical statistical description of data variables based on logical statistical relationships, which can effectively deal with the linear features in the series and requires a low computational cost for modeling [19]. [PERSON] et al. [5] used the ARIMA model to predict monthly garlic prices, and the experimental results showed that the model was effective for the short-term prediction of garlic prices. [PERSON] et al. [6] predicted tomato prices in Turkey by using a seasonal ARIMA model and analyzed the seasonal variation in tomato prices. [PERSON] et al. [7] validated the effectiveness of the ARIMA model for agricultural product price prediction by using rice and other crop prices. [PERSON] et al. [8] proposed that GARCH model had an advantage over the ARIMA model for time series data prediction because it captures the volatility agglomeration existing in price data. [PERSON] [26] predicted soybean, sorghum, and wheat prices in the U.S. through an asymmetric-error-generalized, autoregressive, conditional heteroskedasticity model. [PERSON] et al. [9] applied the GARCH model to the field of garlic price analysis and prediction and found that the series showed spikes and thick tails, volatility aggregation, and heteroskedasticity. Therefore, garlic price prediction can be achieved by capturing and exploiting the price volatility characteristics. The traditional econometric methods can effectively deal with the linear characteristic relationship of a price series, but its disadvantage is that the prediction performance is not stable in nonlinear sequences. Machine-learning methods have been used to predict agricultural prices with complex nonlinear relationships [27; 28; 29]. [PERSON] et al. [13] compared the time delay neural network (TDNN) with the ARIMA model and found that the former enabled better predictions of oilseed price. [PERSON] et al. [30] developed a chaotic neural network to predict weekly egg prices in China and found that the chaotic neural network achieved better nonlinear fitting ability and better prediction performance than ARIMA. [PERSON] et al. [31] developed a back propagation neural network (BPNN) to predict tomato prices. [PERSON] [32] pointed out that shallow neural network structures cannot effectively portray complex nonlinear functions. For this reason, deep learning has been applied to agricultural price prediction. [PERSON] et al. [33] proposed a short-term cucumber price-prediction method based on RNN and found that RNN had better prediction results than BPNN and ARIMA. [PERSON] et al. [14] developed a DLSTM model and found that DLSTM outperformed traditional neural network models and ARIMA model in terms of prediction accuracy and directional changes in price series for nonlinear patterns. [PERSON] et al. [34] proposed LSTM to predict vegetable prices and found that LSTM outperformed various benchmark models, including traditional statistical methods and machine-learning methods in both long-term and short-term prediction. [PERSON] et al. [35] proposed a two-input attention-based LSTM model to predict monthly prices of cabbage and radiishes in the Korean market. Since their study applied both feature and temporal attention mechanisms, the feature correlations and temporal relationships among the input variables can be captured, and the prediction performance was improved compared to the benchmark model. Some scholars have used combination models for agricultural price prediction. [PERSON] et al. [36] predicted the linear and nonlinear components of garlic prices using ARIMA and SVM models, respectively, and finally verified that the combined model outperformed the single ARIMA and SVM models. In addition, other scholars proposed price-prediction methods based on decomposition before integration.