# Research on Price Prediction of Construction Materials Based on VMD-SSA-LSTM
## 6 Conclusions  
Material price prediction is crucial for managing cost risks in engineering projects. It aids in accurately preparing budgets and formulating strategies to optimize the cost structure, ensuring that project budgets remain controllable. By optimizing the supply chain, identifying price risks, and formulating corresponding countermeasures, it effectively reduces the risks of cost fluctuations and ensures project progress. Additionally, it provides a foundation for investment decisions, enabling enterprises to enhance their competitiveness through optimized quotations and improved reputations. Furthermore, it \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(RMSE\) & \(MAE\) & \(R^{2}\)/\% \\ \hline BP & 0.1543 & 0.1271 & 89.966 \\ \hline LSTM & 0.0935 & 0.0681 & 96.315 \\ \hline VMD-LSTM & 0.0932 & 0.0712 & 96.336 \\ \hline VMD-SSA-LSTM & 0.0907 & 0.0699 & 96.532 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction effects of various models. encourages innovation to reduce costs, thereby strengthening enterprises' ability to manage cost risks [3; 7; 9; 10; 11] and facilitating the smooth progression of engineering projects. This paper introduces a novel hybrid prediction model, VMD-SSA-LSTM, for CMP forecasting. Empirical validation and model comparisons show that the proposed model significantly enhances both prediction accuracy and goodness of fit. The findings are highly relevant for stakeholders in the construction industry, particularly in terms of cost risk management, budget planning, and decision-making. The key conclusions are as follows:  
1. A novel framework is developed for CMP forecasting that surpasses traditional methods in both accuracy and stability. By employing VMD to decompose and denoise the data, critical features are extracted, enhancing data quality and mitigating the limitations of single-model approaches that often result from insufficient data learning. Furthermore, SSA addresses issues like rapid convergence and local optima, complementing the LSTM model and significantly improving its predictive performance and robustness. 2. The constructed VMD-SSA-LSTM prediction model shows a reduction in RMSE of 2.68% compared to the VMD-LSTM model, 2.99% compared to the single LSTM model, and 41.22% compared to the BP model. Additionally, the R\({}^{2}\) values increase by 0.196%, 0.217%, and 6.566%, respectively. The model enhances budget accuracy by reducing material price prediction errors by over 40% compared to traditional methods, leading to better financial planning and fewer cost overruns. 3. Risk mitigation and strategic planning: accurate forecasts allow companies to proactively manage risks by locking in prices or adjusting project schedules, minimizing financial uncertainties and delays. 4. Optimized resource allocation and bidding: the model's improved predictive accuracy supports efficient resource allocation, competitive bidding, and strategic procurement, helping firms stay within budget and maintain profitability. This study confirms the practicality of the VMD-SSA-LSTM model in CMP prediction, broadening the application scope of deep learning technologies in this domain. Future research could explore the extension of this approach to other industries or the integration of additional optimization techniques to enhance predictive accuracy further. The proposed hybrid prediction method holds significant reference value for advanced studies in project cost risk management, showcasing strong potential for practical implementation and substantial engineering value. Research ideas, writing of original draft, project administration, empirical analysis, supervision, S.X. Investigation, data search and collection, funding acquisition, J.Z. and Y.L. Supervision, C.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study.