EGCN:基于的图形卷积网络用于在房地产市场中检测和预测异常模式
Dat Le1, Sutharshan Rajasegarar1, Wei Luo1
1School of Information Technology, Deakin University, Geelong, Victoria, Australia.
PloS one
|October 16, 2025
概括
本研究介绍了EGCN,这是一个新的房地产预测框架,将异常区域与正常区域分开. 通过独立分析这些地区,EGCN显著提高了预测的准确性和稳定性.
科学领域:
- 房地产经济学 房地产经济学
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
背景情况:
- 房地产市场表现出由经济,政策和人口因素影响的动态行为.
- 传统的预测模型与市场异常作斗争,导致准确性和稳定性下降.
- 房地产市场的异常区域远远偏离预期趋势,对预测构成挑战.
研究的目的:
- 提出一个新的集群特定预测框架,EGCN,以应对房地产市场预测的挑战.
- 通过独立分析正常和异常市场区域来提高预测的准确性和稳定性.
- 增强市场洞察力,为房地产市场提供更精确,风险调整的预测.
主要方法:
- 开发了EGCN (增强图形卷积网络),用于集群特定的房地产预测的框架.
- 实施异常检测和聚类,以将异常区域与正常区域分开.
- 独立地将预测模型应用于正常和异常区域,评估英国,美国和澳大利亚数据集的表现.
主要成果:
- 与基线和替代异常检测方法相比,EGCN在12,24和48个月的时间范围内实现了最低的预测误差.
- 在检测异常区域方面,EGCN表现出卓越的灵敏度,比竞争方法识别出更多的异常区域.
- 集群异常单独减少了各种模型的预测错误,包括时间序列预测神经层次插曲,提高了准确性和稳定性.
结论:
- 通过对待正常和异常地区的区别,EGCN框架有效地提高了房地产市场预测.
- 单独聚类异常允许预测模型更好地捕捉各种市场行为,从而产生更准确和更强大的预测.
- EGCN在房地产市场分析方面取得了重大进展,提供了更精确和风险调整的预测能力.
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