# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 4 Results
### Forecast Analysis
#### 4.2.1 Prediction Results from a Single Model  
This subsection compares the effect of each single model with or without denoising techniques in terms of predictive performance.  
Figure 4: The smoothed price series for scallions in the test set.  
Different signal decomposition techniques can reveal unique patterns in time series data. The high-frequency components usually capture the effects of random factors, while the mid-frequency components reflect the role of cyclical factors, and these components show a distinct sinusoidal waveform. The low-frequency components, on the other hand, represent trends in price changes over time. By reconstructing these decomposition sequences, the noise in the data can be effectively removed, thus improving the prediction accuracy while reducing the computational complexity. Table 5 shows the performance evaluation results of different single models with or without various decomposition techniques, where the best performing results are shown in bold and the respective MAPE results are presented in Figure 5.  
First, in terms of decomposition techniques, the SSA method has the best decomposition results. Compared with the single model without decomposition, the MAPE of scallions is reduced by 41.94% on average, and this improvement can be attributed to the precise identification of data features by singular spectrum analysis (SSA), which helps to reveal the potential drivers of price volatility of agricultural products. The EMD decomposition technique is the next most effective, with an average MAPE reduction of 25.95%. The VMD is the least effective, with an average MAPE reduction of 17.70%. Overall, the application of decomposition and denoising techniques did significantly enhance the performance of the single models in scallion price prediction; however, for those models with poor model stability, too much model decomposition may trigger the accumulation of errors, which may negatively affect the accuracy of the prediction results.  
Second, the prediction effects of the three single models are roughly the same, while the decomposition ensemble models mixed with SSA, EMD, and VMD decomposition techniques improve the prediction performance of the single models to different degrees. The use of decomposition denoising techniques can effectively capture trends and fluctuations in the data, thus improving the predictive ability of the models. On average, the ARIMA, LSTM, and GRU models with decomposition techniques have their directional accuracy improved by 54.39%, 43.53%, and 38.89%, respectively, and their prediction accuracy MAPE reduced by 32.80%, 32.73%, and 20.97%, respectively, compared to the single model. It can be seen that, with the same decomposition technique, the prediction accuracy of the ARIMA-based single model is significantly improved in terms of the effect of the directional transformation of the predicted price and the prediction accuracy compared to the LSTM- and GRU-based models. And the prediction effects of the two intelligent models are approximately the same. In summary, SSA-ARIMA is the most effective in predicting the price of scallion, with a 33.02% improvement in MAPE and a 10.50% improvement in D\({}_{\text{stat}}\) compared to the average results of all other models. These results indicate that  
Figure 5: MAPE of individual models in predicting scallion prices.  
the application of this decomposition denoising and reconstruction technique enables the model to capture the details of price fluctuations more accurately and thus performs better in predicting short-term fluctuations and long-term trends in prices. In addition, the decomposition and reconstruction technique can also help the model identify and filter out noise and outliers in the price series, further improving the accuracy and reliability of the forecasts.