Grey Forecast Technology And Its Application Research

In research of system, because of the existence of internal and external disturbances and the limited level of awareness, people get information with some uncertain. With the scientific and technological of development and progress of human society, people gradually deepen to understand uncertainty of all kinds of systems, and strengthen to research system's uncertainty. Grey systems theory is developed to study problems of"small samples and poor information". These problems studied by grey systems theory cannot be handled successfully by using either probablitity statistics or fuzzy mathematics.Grey systems theory looks for realistic patterns based on modeling a few available data. Different from fuzzy mathematics, grey systems theory focuses on such research objects that have clear extension and unclear intension.Grey system theory explores reality regulity through the generation of information, development, and extraction of valuable information. Grey systems theory has no special requirements and restrictions on data sequence. So its application is very broad. Grey prediction is an important component of grey system theory, but also a very active research field. Mainly from the initial value, the background value, grey derivative, discretization, model parameters and the pathological point of view, etc. the existing literatures have achieved fruitful results. But there are still many theoretical problems needed to be resolved as soon as possible. This paper aims to study the basic theories of grey prediction thechnology. According to the grey system theory in the buffer operator theory, some new buffer operators have been constructed through the grey sequence generated to satisfy the three buffer operator axioms. Summarized conditions of existing data transformation technology, the corresponding data transformation technologies are proposed. The paper studies how to model with the oscillation sequence of non-monotonic system. Grey models are proposed based on the theory of continued fractions GM (1,1) model and the theory of vector continued fractions MGM (1, n) model. The main innovations of the paper are follows.The first innovation is to construct some new buffer operators with clear phisical meanings. Buffer operator theory is an inpormant aspect of grey system theory and one of the main features of the theory. Grey system theory seeks the laws of a system, such as the social, economic, ecological systems, which is a kind of data from the data to find the rules. At present, the study on the buffer operators is basically divided into two aspect: rebuilding the buffer operators and application of the existing buffer operators to solve practical problems. In this paper, some new buffer operators are constructed with economic sigificance based on grey system theory "the new information priority" principle and the theory of time series. They can weaken some randomness to show regularity successfully by excluding the impact of external interference. So stability and prediction accuracy of grey prediction model are improved.The second innovation is to data transformation technology. Data transformation technologies as a method to improve prediction accuracy of grey model one of the methods are effective. The paper comprehensively analysizes factors of data transformation technology to improve prediction accuracy of grey model, and shows that the choice of data transformation technology should be considered as a whole. Smooth ratio, stepwise ratio, convex-concave and reductive error should be considered. The paper takes these factors as the principles of data transformation technology, and proposes two forms of data transformation technology. These two forms improve the prediction accuracy and applicability of grey model. Also the existing data transformation technolgies are analysized.The third innovation is to how to model with the oscillation sequence of non-monotonic system. When the original data sequence features with some fluctuations, to build GM (1, 1) model for the simulation of access to higher forecast accuracy. When the raw data rate volatility is not a big swing and the raw data in this article series on the proper handling of the fluctuations in the original data sequence into a monotonous sequence of growth, and then establishment of GM (1,1) model, and to study some properties of the model. In addition, grey discrete Verhulst model is proposed with the rapid growth in the fist part and slow growth in the second part of data sequence. The Verhulst model is mainly used to study processes with saturated states (or say sigmoid processes).The last innovation is to combine grey prediction model with continue fraction theory. Because of uncertainty of system, there is sensitivity to the setting form of traditional single model. Therefore, only using the traditional grey forecasting model is often difficult to achieve the desired prediction. As we know, Different theories and methods from different physical backgrounds, are to solve a subset of real life problem encountered. And these theories and methods are not mutually exclusive, but interrelated and mutually complementary. These theories and methods have their own characteristics and features to excavate from a different system, useful information which is very important for accurate prediction. So grey models are proposed based on the theory of continued fractions GM (1,1) model and the theory of vector continued fractions MGM (1, n) model.