现代信息科技2024，Vol.8Issue(5)：102-105,110,5.DOI:10.19850/j.cnki.2096-4706.2024.05.022
基于PSO-LSTM的区域二手房价预测方法研究
Research on Regional Second-hand Housing Price Prediction Method Based on PSO-LSTM
摘要
Abstract
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.关键词
区域二手房价预测/时间序列/PSO-LSTM模型/LSTMKey words
regional second-hand housing price prediction/time series/PSO-LSTM model/LSTM分类
信息技术与安全科学引用本文复制引用
周昌堉,李长云..基于PSO-LSTM的区域二手房价预测方法研究[J].现代信息科技,2024,8(5):102-105,110,5.
