scholarly journals Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Sets via Delegation of Hesitancy Degree to the Major Grade De-i-fuzzification Method

2021 ◽  
Vol 9 (1) ◽  
pp. 46-53
Author(s):  
Nik Muhammad Farhan Hakim Nik Badrul Alam ◽  
Nazirah Ramli ◽  
Norhuda Mohammed
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Intuitionistic Fuzzy Sets ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Model Based ◽  
Intuitionistic Fuzzy

A fuzzy time series forecasting method induced by intuitionistic fuzzy sets

2015 ◽  
Vol 06 (04) ◽  
pp. 1550041 ◽  
Author(s):  
Sanjay Kumar ◽  
Sukhdev Singh Gangwar
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Time Series Data ◽  
Financial Time Series ◽  
Intuitionistic Fuzzy Sets ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Intuitionistic Fuzzy ◽  
Forecasting Method

Intuitionistic fuzzy sets (IFSs) are well established as a tool to handle the hesitation in the decision system. In this research paper, fuzzy sets induced by IFS are used to develop a fuzzy time series forecasting model to incorporate degree of hesitation (nondeterminacy). To improve the forecasting accuracy, induced fuzzy sets are used to establish fuzzy logical relations. To verify the performance of the proposed model, it is implemented on one of the benchmarking time series data. Further, developed forecasting method is also tested and validated by applying it on a financial time series data. In order to show the accuracy in forecasting, the method is compared with other forecasting methods using different error measures.

scholarly journals Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Ya’nan Wang ◽  
Yingjie Lei ◽  
Xiaoshi Fan ◽  
Yi Wang
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Intuitionistic Fuzzy Set ◽  
Time Series Forecasting ◽  
Approximate Reasoning ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Forecasting Model ◽  
New Model ◽  
Intuitionistic Fuzzy

Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

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