Research On Combination Forecasting Model And Algorithm Based On Multiple Crossover And Cross Section Variance Weighting

Early warning is to remind people to pay attention and vigilance in advance,which is the product of organic integration of prediction and evaluation.It has been widely used in the economic and social fields.The Yellow early-warning index strategy is suitable for the complex early warning in the economic and social fields.If the extrapolation paradigm is used to measure the degree of alarm,the prediction of the alarm in the future period is the core task.The degree of alarm is a comprehensive index to measure the extent of an object that deviates from the expected state in the process of development.It can be seen that the sample of the alarm forecast is not the entity data,but the virtual data generated by the entity data.Because of many methods for generating synthetic index can be used to calculate and evaluated alarm degree,and many time series prediction methods can be used to measure alarm characteristics(small samples and atypical distribution characteristics).However,with the combination of different calculation methods and different time series prediction methods,the prediction of future period of alarm is diverse.The warning degree of the critical point of the alarm signal level is usually selected by the group decision method,but it is unique when it is applied.It can be seen that when the different alarm measurement and time series prediction methods are adopted,the warning signals may be different and affect the credibility of early warning.This paper is according to the efficacy and prediction theory and method of warning degree,based on the normalization of the sample of the feature attribute sequence of the early-warning object,puts forward a system structure of warning degree of combination forecasting,implementation way and effect minimum datum selection strategy and a data fusion method of cross-sectional variance weight are also proposed to build a uniform scale grey correlation method,vector angle cosine method and information entropy method.By discussing exact and consistent time series prediction methods such as grey GM(1,1),three exponential smoothing and Markov chain,we built and clarified a warning combination forecasting control mechanism and process algorithm.Taking the prediction of regional eco economic development as an example,a combination forecasting model system is established,and the control mechanism and process algorithm are applied to verify the effectiveness and adaptability of the combination forecasting method.The empirical application shows that different kinds of single prediction methods often predict two or more kinds of alarm signals in the same time period.Only by combining prediction can we make the prediction of alarm signal unique,and ensure early warning credibility.The correlation between the combined prediction results and the early warning attribute samples is greater than 0.95,so the prediction is highly reliable.The absolute value of the relative error of combined prediction and single prediction is less than 5%,so the accuracy of the prediction is high.Combined prediction effectively maintains the change characteristics of single prediction and ensures the comparability of prediction.