# Research on Price Prediction of Construction Materials Based on VMD-SSA-LSTM
## 5 Model Application and Results Discussion  
### Model Application Comparison  
This paper uses the experimental sample data for the Hunan Housing and Construction Department cost management station, Hengyang City, for 1 October 2020-31 May 2024 based on the re-bar HRB400E 18-25 half-monthly market information price. Using the same experimental data, it is necessary to divide the dataset, with the training set accounting for 70% and the testing set accounting for 30%. Then, the researcher should input the processed experimental data into the established model for prediction to compare and verify the prediction effect and prediction accuracy of the coupled model. First, a BP neural network prediction model with a topology of 8-5-1 and a single LSTM prediction model are established for prediction. The results and prediction errors of the models are shown in Figures 7 and 8, respectively. Regarding the prediction results, comparing Figure 7a,b reveals that both the BP model and the LSTM model can approximately predict the change trend of CMP. However, the LSTM model in Figure 7b aligns significantly better with the fluctuation trend of material prices compared to the BP model in Figure 7a. This indicates that the predictive performance of the LSTM model is superior to that of the BP model. In terms of prediction error, the BP model's error range shown in Figure 8a is \([-0.5102,\,0.4144]\), while the LSTM model's error range in Figure 8b is \([-0.3929,\,0.1761]\). The length of the prediction error interval for the BP model is \(0.9246\), whereas for the LSTM model it is \(0.5690\). This means that the prediction error of the LSTM model is reduced by \(38.46\%\) compared to the BP model. The variation range of the error interval for the LSTM model is significantly smaller, indicating that the LSTM model has higher prediction accuracy and greater stability in CMP prediction compared to the BP model. Figure 7: Prediction results of four models. (**a**) The graph represents the trend of change between the predicted and true values of the BP model, i.e., the BP model fit. (**b**) The graph represents the trend of change between the predicted and true values of the LSTM model, i.e., the LSTM model fit. (**c**) The graph represents the trend of change between the predicted and true values of the VMD-LSTM model, i.e., the VMD-LSTM model fit. (**d**) The graph represents the trend of change between the predicted and true values of the VMD-SSA-LSTM model, i.e., the VMD-SSA-LSTM model fit. Secondly, the above comparison of single-model predictions shows that while the LSTM model has clear advantages for predicting time series data, its prediction accuracy still does not meet the expected level in the experiment and remains relatively low. To enhance prediction accuracy, VMD is employed to decompose and denoise the original data sequence, thereby improving data quality and achieving better prediction accuracy. Regarding prediction effect, the VMD-LSTM model in Figure 7c does not show significant improvement compared to the LSTM model in Figure 7b; it may even be considered a case of negative optimization. However, in terms of prediction error, the error range of the LSTM model in Figure 8b is [\(-0.3929\), \(0.1761\)], while the error range of the VMD-LSTM model in Figure 8c is [\(-0.0716\), \(0.0327\)]. The prediction error interval length for the LSTM model is \(0.5690\), and for the VMD-LSTM model it is \(0.1043\). The prediction error of the VMD-LSTM model is thus reduced by \(81.66\%\) compared to the LSTM model. The variation range of the error interval for the VMD-LSTM model is significantly smaller, indicating that the model is more stable. Simultaneously, to avoid the negative impact of the observed negative optimization while improving prediction accuracy, SSA is used to optimize the hyperparameters of the LSTM model. The experimental results for the VMD-SSA-LSTM model are shown in Figures 7d and 8d.