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Home sales index prediction model based on cluster and principal component statistical approaches in a big data analytic concept

机译:大数据分析概念中基于聚类和主成分统计方法的房屋销售指数预测模型

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摘要

Data analysis has become one of the most important tools in various fields of engineering. HSI (Home Sales Index) has been considered one of the most crucial factors for predicting the economic trends in the construction industry. While a precise prediction of a HSI is very significant, it is limited due to the difficulty in collecting valuable information and analyzing that information properly. The several conducted studies related to a similar subject have mostly approached to the time series analysis with assumption that the HSI has been influenced with the past patterns such as seasons and historic issues. This paper mainly focused on the objective of presenting the performance of a new HSI prediction model using a big data analysis method focusing on social factors in high correlations in HSI without any those assumptions. In accordance with this research objective, this paper suggests a definite methodology for analyzing data and developing a related prediction model using cluster and principal component statistical approached focusing on comprehensive search queries on the Web. The significant results derived from the model applied to HSI prediction can be used as the initial phase of a big data analysis in the construction field in both academia and industry.
机译:数据分析已成为工程各个领域中最重要的工具之一。 HSI(房屋销售指数)被认为是预测建筑行业经济趋势的最关键因素之一。尽管对HSI的精确预测非常重要,但由于难以收集有价值的信息和正确分析该信息而受到限制。与类似主题相关的几项研究大多以时间序列分析为基础,并假设恒生指数受到过去模式(如季节和历史问题)的影响。本文主要关注使用大数据分析方法介绍新的HSI预测模型的性能的目标,该方法侧重于HSI中高度相关的社会因素,而无需任何假设。根据这一研究目标,本文提出了一种确定的方法,该方法用于分析数据并使用聚类和主成分统计方法开发相关的预测模型,并着重于Web上的综合搜索查询。从应用于HSI预测的模型得出的重要结果可以用作学术界和工业界建筑领域大数据分析的初始阶段。

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