摘要
Fuzzy sets theory cannot describe the neutrality degree of data, which has largely limited the objectivity of fuzzy time series in uncertain data forecasting. With this regard, a multi-factor highorder intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to get unequal intervals, and a more objective technique for ascertaining membership and non-membership functions of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on multidimensional intuitionistic fuzzy modus ponens inference are established.Finally, contrast experiments on the daily mean temperature of Beijing are carried out, which show that the novel model has a clear advantage of improving the forecast accuracy.
Fuzzy sets theory cannot describe the neutrality degreeof data, which has largely limited the objectivity of fuzzy time seriesin uncertain data forecasting. With this regard, a multi-factor highorderintuitionistic fuzzy time series forecasting model is built. Inthe new model, a fuzzy clustering algorithm is used to get unequalintervals, and a more objective technique for ascertaining membershipand non-membership functions of the intuitionistic fuzzy setis proposed. On these bases, forecast rules based on multidimensionalintuitionistic fuzzy modus ponens inference are established.Finally, contrast experiments on the daily mean temperature ofBeijing are carried out, which show that the novel model has aclear advantage of improving the forecast accuracy.
作者
Ya'nan Wang
Yingjie Lei
Yang Lei
Xiaoshi Fan
Ya’nan Wang Yingjie Lei Yang Lei Xiaoshi Fan(Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China Department of Electronics Technology, Engineering University of Armed Police Force, Xi’an 710051, China)
基金
supported by the National Natural Science Foundation of China(61309022)
