## 4 Experimental Procedure and Analysis of Results
### Price Prediction Analysis  
According to the above research on garlic-price-prediction methods, the combined LSTM-GP model has the highest prediction accuracy. To further verify the effectiveness of the model, we predicted garlic prices for a total of 31 days from 15 May 2021 to 14  
Figure 9: Comparison of MAE, RMSE, and MAPE among the proposed optimal model, the GRU–GARCH models, the CNN–GARCH combined models, and the attention–LSTM model. (**a**) Comparison between the proposed optimal model and the GRU–GARCH family combined models. (**b**) Comparison between the proposed optimal model and the CNN–GARCH family combined models. (**c**) Comparison among the proposed optimal model, attention–LSTM model, and LSTM model. June 2021, by using LSTM-GP, and analyzed the prediction performance. A comparison of the predicted results with the actual prices is shown in Figure 10; and MAE, RMSE, and MAPE of the models are shown in Figure 11. MAE, RMSE, and MAPE were 0.1143, 0.1400, and 4.40%, which are 16.93%, 17.93%, and 17.47% lower than those of the LSTM model. From Figure 10, we can see that the predicted values of the combined LSTM-GP model are closer to the actual prices than those of LSTM. This also confirms that the price series features extracted by the GARCH-family models help to improve the prediction performance of the model. Through empirical analysis, it can be concluded that the model is effectively and stably applicable to the short-term prediction of garlic prices and has a certain degree of interpretability to support the prediction of garlic prices. improving the model's prediction performance. Large-scale experiments based on real data led to the following conclusions. (1) Among the single models, the LSTM model performs significantly better than the GARCH-family models. By using its long memory property to effectively learn the temporal patterns of time series, LSTM can deal with the complex nonlinear relationships in garlic-price series and overcome the shortcoming of low accuracy in traditional prediction models. (2) The prediction performances of the combined models were generally better than those of the single model. The combined models of the LSTM and GARCH family introduce the statistical features of garlic price data into the deep-learning model and reasonably exploit the linear and nonlinear information in the price data. The combined models outperformed the single models in the evaluation indexes of mean absolute error, root mean-square error, and mean absolute percentage error. (3) The LSTM-GP combined model, which incorporates GARCH, PGARCH, and LSTM models, had the best performance of all models. The empirical analysis concluded that the model is effectively applicable to the short-term prediction of garlic prices and has a certain degree of interpretability, which can provide support for garlic price prediction. In light of the results obtained, we have proposed a combined model that is applicable to garlic price prediction called LSTM-GARCH. The model proposed in this paper, which was studied from a time series perspective, solves the problems of low prediction accuracy of the traditional single model and poor model interpretability of existing price prediction models. It can effectively achieve the short-term prediction of garlic prices. The accurate prediction of garlic prices can help people related to the garlic industry chain to make correct judgments and scientific decisions, which is conducive to avoiding market risks and can play a guiding role in stabilizing the market for this small agricultural commodity. Currently, the combined model we have constructed has not been put into use. [PERSON] et al. [51] proposed a broad set of intelligent agents that achieve cryptocurrency prediction and can be used for actual trading. Inspired by this, we are considering further completing the garlic price prediction system in the garlic industry chain big data platform we developed.