Research On The Methods And Applications Of Forecasting And Decision Making Based On Unascertainty

The uncertainty of objective is divided into four kinds in this paper. There are randomness, fuzziness, greyness and unascertainty. Among them the former three kinds of uncertainty have been researched widely and the fourth kind uncertainty has received significant attention in recent years. And a number of valuable results and applications of unascertainty have been obtained. But little work has been carried out for the applications of unascertainty in forecasting and decision making. This paper will research the applications of unascertainty in forecasting and decision making. Except the foreword, basic knowledge of unascertained mathematics and conclusion, this paper includes the following three parts: Part One is the basic methods of unascertained forecasting and combining forecasting (Chapter 2 and Chapter 3). In Chapter 2, the unascertained linear regression model with unascertained regression parameter is presented and its parameter estimation method is given firstly. The unascertained classfiy regression forecasting is given secondly. And the unascertained clustering forecasting is given finally. In Chapter 3, we use the combining forecasting model to deal with the different kinds of uncertainty information of the forecasting problem. Three kinds of generalized weighted mean combining forecasting models and their parameter estimation methods are proposed. And their efficiencies are demonstrated by examples. Part Two is the methods of unascertained decision making and unascertained optimization (Chapter 4 and Chapter 5). In Chapter 4, the analytic hierarchy process based on the unascertained three-valued judgments is presented and its method to check the consistency degree of the pairwise comparisons judgment matrix and the calculation method of weighting coefficients are given. Then, the unascertained measure evaluating method is given and several objective methods of determining the weights are given. In Chapter 5, a new decision model of investment combination on venture capital by using the unascertained mathematics is given for the future income of venture capital is unascertained. The coefficients of the general partners on risk and income are given by using the relative entropy aggregation model of group decision making. And the method of weighting coefficients of venture capital investment combination is given. Then, the all coefficient unascertained linear programming with equations is discussed and its solution method is given. And an application example is given. Part Three are the applications of unascertained forecasting and unascertained decision making (Chapter 6 and Chapter 7). In Chapter 6, the application of unascertained forecasting in air material consumption is given. Three kinds of generalized weighted mean combining forecasting models of air material consumption based on grey system and unascertained clustering forecasting are proposed and their efficiencies are demonstrated by examples. In Chapter 7, the application of unascertained decision making in venture capital is given. Firstly, the unascertained analytic hierachy process is used to choose the venture capital items. Then, an index system for evaluating the non-systematic risks of venture capital investments is established on the basis of analysing the factors affecting the non-systematic risks of venture capital investments. And the unascertained measure method for evaluating the risks of venture capital investments is presented by applying the unascertained measure theory. Finally, the model for controlling the risks of venture capital investments is established on the basis of the results of the unascertained measure evaluation of venture capital investments. An example of practical application is given to show the effectiveness of this method.