# Research on Price Prediction of Construction Materials Based on VMD-SSA-LSTM
### Current Status of Research
Subsequently, they pushed the data into different models, namely the LSTM network model with the same number of hidden neurons in different LSTM layers and the LSTM network model with different numbers of hidden neurons in the same LSTM layer, to perform stock price prediction and analysis. [PERSON] et al. [23] used more than 7000 pieces of SSE Composite Index data and the LSTM neural network model to predict and analyze the SSE Composite Index. [PERSON] et al. [24] found that traditional statistics- and econometrics-based time series forecasting models have certain limitations in terms of long-term dependence, constructed a multilayer LSTM network price forecasting model, and used China's soybean futures price data from 2007-2019 to carry out empirical research, and the results showed that the LSTM network model performs well in price forecasting. Despite its advantages in capturing complex nonlinear relationships, LSTM still faces challenges in handling noisy data and optimizing model parameters. Existing research highlights significant advancements in addressing fluctuations in construction material prices through the development of various forecasting methods. Ranging from traditional statistical models to advanced AI-based algorithms, these approaches have aimed to enhance forecasting accuracy. However, each method exhibits inherent limitations. Traditional time series models, such as ARIMA, rely on assumptions of linearity and stationarity, rendering them less effective at capturing complex nonlinear and non-stationary features. Conversely, artificial neural networks (e.g., LSTM) demonstrate strong capabilities in modeling complex nonlinear relationships but face challenges in handling noisy data and optimizing parameters. To address these limitations, hybrid models have emerged as a prominent research focus in recent years. For instance, [PERSON], [PERSON] et al. [25] optimized the concrete strength prediction model by integrating a genetic algorithm (GA) with a BP neural network. The GA was employed to optimize the neural network's weights and biases, enhancing both prediction accuracy and stability. The study's results demonstrated that the GA-BP neural network model effectively predicted the strength of recycled aggregate thermal insulation concrete, exhibiting high accuracy and practicality. [PERSON] et al. [26] utilized historical price data for construction materials to develop a grey neural network (PGNN) model by combining the GM (1,1) with a BP neural network in MATLAB. This approach significantly improved forecasting stability by leveraging the complementary strengths of the two methods. Similarly, [PERSON] et al. [27] employed a genetic algorithm to optimize a neural network model using specialized software, thereby enhancing prediction accuracy. [PERSON] et al. [28] proposed a forecasting approach based on least-squares support vector machine, optimized with improved particle swarm optimization (IPSO). By rigorously training and testing the LSSVM model, they achieved substantial improvements in predicting future material prices. In the aforementioned studies, most authors utilized mathematical statistical methods and neural network approaches to construct models for CMP prediction. It is evident that the prediction accuracy of a single model can achieve allow us to good results, but there is still room for further improvement. To enhance the predictive accuracy of the models, many authors employ optimization algorithms for model optimization. Experimental results indicate that the predictive accuracy of the optimized models has significantly improved. Recent advancements in hybrid modeling have shown promise in enhancing prediction accuracy by addressing the limitations of individual models. While neural network-based approaches, such as Long Short-Term Memory (LSTM) networks, capture nonlinear dependencies effectively, their predictive performance can degrade in noisy and non-stationary environments.