Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application

Abstract

In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system, a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error, the change tendency of the forecasted object, gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight. Based on Visual Basic 6.0 platform, a fuzzy adaptive variable weight combined forecasting and management system was developed. The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.

Keywords

nonlinear combined forecasting / nonlinear time series / method of fuzzy adaptive variable weight / relative error / adaptive control coefficient