# Research on Price Prediction of Construction Materials Based on VMD-SSA-LSTM
### Current Status of Research  
Studies have shown that various methods, including time series analysis, support vector machine regression (SVR), gray prediction (GM), system dynamics (SD), and neural network models, are widely used for forecasting construction material prices. In response to price fluctuations, researchers and industry experts have developed a range of forecasting approaches, such as statistical models (e.g., ARIMA), machine learning techniques (e.g., Long Short-Term Memory [14] (LSTM)), and hybrid methods. Each of these methods offersdistinct advantages and limitations, making them suitable for different types of data and forecasting objectives. BP neural networks (backpropagation neural networks) are widely used in nonlinear prediction tasks due to their strong nonlinear fitting capability. For instance, [PERSON] et al. [15] applied BP neural network to establish a price prediction model for construction materials to realize the prediction of CMP trends. The prediction results are basically consistent with the actual prices. [PERSON] et al. [16] proposed taking the historical price information of materials as a sample and constructing a model with the help of BP neural network to predict the future trend of CMP. However, BP networks are prone to falling into local optima, require long training times, and demand large amounts of data. Grey model (GM (1,1)) prediction is well suited to handling small sample datasets and can even make predictions when data are limited. For example, [PERSON] [17] predicted the steel prices from June to December 2012 by establishing a GM (1,1) and taking the market steel price data of Zhengzhou city from January to July 2012 as an example. However, GMs (1,1) are highly dependent on the available data, and their prediction accuracy is often limited. ARIMA (autoregressive integrated moving average) is a well-established time series forecasting method that models data based on autocorrelation. It is widely used for short-term predictions, particularly for stationary time series. While ARIMA is simple and easy to understand, it has limitations in handling nonlinear relationships and long-term dependencies. For instance, [PERSON] et al. [18] employed ARIMA and multiple regression models to predict CMP. [PERSON] et al. [19] used the time series method to forecast the short-term price using the historical cost information of steel prices, and based on the ARIMA model, they predicted the steel prices over the next three months and achieved good prediction results. [PERSON] et al. [20] utilized the time series method to predict and analyze the historical price data of construction materials accumulated in a construction cost information database. [PERSON] et al. [21] used the autoregressive integrated moving average (ARIMA) model to predict the sales data of dehumidifiers and heat pumps in the projects extracted from the website. [PERSON] et al. [12] utilized artificial neural network (ANNs) to quantify uncertainty through generating prediction intervals, thereby achieving the purpose of predicting the price range of construction materials. In contrast to ARIMA, artificial neural networks (ANNs) are inspired by the human brain's structure and are capable of modeling complex nonlinear patterns through dynamic learning. However, ANNs are prone to overfitting and require large datasets and significant computational resources. More recently, Long Short-Term Memory (LSTM) networks, using a deep learning technique, have demonstrated superior performance in time series forecasting. [PERSON] et al. [22], by combining the characteristics of LSTM (Long Short-Term Memory) recurrent neural networks and those of the stock market, preprocessed data through interpolation, wavelet noise reduction, and normalization.