Research on Regional Second-hand Housing Price Prediction Method Based on PSO-LSTM
Exploring the trend of housing prices is a highly complex and full of nonlinear features research challenge.Aiming at the current problem of low accuracy of second-hand housing price prediction,this paper proposes a regional second-hand housing price prediction method based on PSO-LSTM.The Particle Swarm Optimization optimizes the LSTM model to find the optimal parameter group and incorporate it into the PSO-LSTM model,and then get the prediction results that are more in line with the actual situation.In this paper,the PSO-LSTM model is trained by the time series dataset of second-hand housing price in Tianyuan District,Zhuzhou City,Hunan Province,and the PSO-LSTM model is analyzed against the LSTM neural network model.The experimental results show that the PSO-LSTM model has better prediction accuracy for regional second-hand housing prices.
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