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首页> 外文会议>IEEE International Conference on Big Data Analytics >The Principal Component Linear Regression Prediction Model of the Impact of Enterprise Patent Activities on the Income of Main Business: A Case Study of Tianjin,China
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The Principal Component Linear Regression Prediction Model of the Impact of Enterprise Patent Activities on the Income of Main Business: A Case Study of Tianjin,China

机译：企业专利活动对主营业务收入影响的主成分线性回归预测模型：以天津为例

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摘要

In order to construct a big data image of enterprise science and technology economic activities and predict the development prospects of science and technology enterprises, this paper takes the data of Tianjin science and technology enterprises as a sample, and uses the linear regression method of principal component factor analysis to establish a patent activity of science and technology enterprises-five years later The business income forecasting model has a good effect on actual testing and can be used as a reference for evaluating and predicting the future economic benefits of the company from existing data. This paper provides a feasible direction for designing a dynamic monitoring platform for predicting the development of technology-based enterprises based on the sample model based on big data and multi-method fusion.
机译：为了构建企业科技经济活动的大数据形象，预测科技企业的发展前景，以天津市科技企业数据为样本，采用主成分线性回归方法五年后建立科学技术企业专利活动的因素分析该业务收入预测模型对实际测试具有良好的效果，可以用作根据现有数据评估和预测公司未来经济收益的参考。本文基于大数据和多方法融合的样本模型，为技术型企业的发展动态预测平台的设计提供了可行的方向。

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