Predicting Public Housing Prices Using Delayed Neural Networks

被引：0
|
作者：
Wang, Lipo [1 ]
Chan, Fung Foong [1 ]
Wang, Yaoli [2 ]
Chang, Qing [2 ]
机构：
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore
[2] Taiyuan Univ Technol, Coll Informat Engn, Taiyuan, Peoples R China
来源：
PROCEEDINGS OF THE 2016 IEEE REGION 10 CONFERENCE (TENCON) | 2016年
关键词：
Artificial neural network ( ANN); public housing prices; estimate; Singapore; TIME-SERIES; CANCER CLASSIFICATION; SELECTION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This study uses delayed neural network models to predict public housing prices in Singapore. The delayed neural networks are used to estimate the trend of the resale price index (RPI) of Singapore housing from the Singapore Housing Development Board (HDB), with nine independent economic and demographic variables. The results show that the delayed neural network model is able to produce a good fit and predictions.
引用
收藏
页码：3589 / 3592
页数：4
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