Grey Principal Component Analysis And The Research On Multiple Indicators Time Series In The Comprehensive Evaluation

The large quantities of statistical data often need be processed real-timely in many fields, for example the economy, finance, control and decision-making, but in recent years, with the emergence of the large number of multiple indicators time-series data, which puts forward higher requirements for accuracy and reliability of the statistical methods. Principal component analysis is often used to deal the multiple indicators data. But the method can't timely and accurately reflect the change characters and the trends of the time series, and can't supply the right decision-making. However, the study of the grey system theory mainly bases on the cumulative time series, which emphasizes the new data optimization and researches on real regulation.This paper is on the basis of principal component analysis method, combining with the cumulative thinking of grey theory. We put forward a new method, which is grey principal component analysis method for solving the comprehensive evaluation problems of multiple indicators time series data to make up the principal component analysis'deficiencies. The studies and the results of this paper obtained are as follows:1. We have studied on comprehensive evaluation methods and principal component analysis deeply, summarized the current methods. We find its disadvantages on the basis of doing a lot of experiments.2. According to its disadvantages, searching the related books and literatures to find that the grey system theory is fit for solving the issues of the time series, therefore, according to the grey degree and cumulative thinking, this paper redefines the basic statistical characteristics of the principal component analysis: mean and variance, covariance and correlation coefficient.3. This paper presents the basic idea, steps, the algorithm processes of the grey principal component analysis method, and gives the simulation examples, which uses the different methods to analysis the data, so that can prove the effectiveness of the method we propose in this paper, the result of the analysis is more rational and accurate; On the other hand, the method can timely, accurately reflect the change characters and the trends of the time series, revealing the dynamic structure and the regulation of the systems according to the dynamic data. It can extract the accurate information that our needs as much as possible, so that can supply the decision support for the future.4. On the basis of the algorithm this paper proposes, we use Microsoft Visual C + +. Net development tools to design and implement the grey principal component analysis computing platform basing on windows. 5. Using two different examples as test data to test the algorithm, comparing with the different methods, the results show that this method in dealing with the multiple indicators time series data is more effective, which can provide the valuable, accurate decision-making. The results of the experiments show that the grey principal component analysis method which this paper puts forward plays the important role in the statistical indicators time series data problems and has the effects for the future research.