Research and Application of Grey Factor Analysis in Big Data Analytics

Predictive analysis is one of the most important application fields of big data analytics, which can predict uncertain events by combining various advanced analysis functions, especially statistical analysis and other methods. Big data analytics technology is mainly applied in business intelligence, government decision-making, public services and so on. Factor analysis is one of the methods in classical statistics, which can only give the distribution degree and variation of data based on the mean value, and can not reflect the characteristics of data objects changing with time, so it is not suitable for solving the statistical analysis problem of big data objects based on time series. In order to make up for the deficiency of factor analysis of big data with time series, according to the characteristics of time series in grey system, that is, grey system theory devotes itself to the discussion of realistic laws through the generation and development of sequences, while mathematical statistics devotes itself to the study of statistical historical laws. Combining the advantages of grey system and classical factor analysis, this paper proposed a new model, namely grey factor analysis applied on bigdata analysis layer, and implemented systematically by using object-oriented programming method. Finally, this method is applied to big data analytics. The results are analyzed and compared, and the effect is remarkable and best forecasting. The experimental results show that this method has promoted the analysis and research of big data with time series, and plays an important role in refining the value of complicated massive data, predictive analysis and data mining forms to help the government better understand the data and draw predictive decisions according to the results of data mining.