Fuzzy time series forecasting based on axiomatic fuzzy set theory

2018 ◽  
Vol 31 (8) ◽  
pp. 3921-3932 ◽  
Author(s):  
Hongyue Guo ◽  
Witold Pedrycz ◽  
Xiaodong Liu
Keyword(s):  
Time Series ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Axiomatic Fuzzy Set Theory

An advisable facial semantic characterization based on Axiomatic Fuzzy Set theory and information granules

2020 ◽  
Vol 523 ◽  
pp. 133-151
Author(s):  
Yan Ren ◽  
Shuting Zhang ◽  
Wanquan Liu ◽  
Lidong Wang ◽  
Wei Guan
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Information Granules ◽  
Axiomatic Fuzzy Set Theory

The Modeling of Interval-Valued Time Series: A Method Based on Fuzzy Set Theory and Artificial Neural Networks

2019 ◽  
Vol 18 (01) ◽  
pp. 1950002
Author(s):  
Wei Zhou ◽  
Dan Shan ◽  
Jianhua Yang ◽  
Wei Lu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Experimental Studies ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Interval Valued

Interval-valued time series (ITS) are interval-valued data that are collected in chronological order. The modeling of ITS is an ongoing issue in domain of time series analysis. This paper presents a new modeling method of ITS based on the synergy of fuzzy set theory and artificial neural networks. The proposed method involves the construction of collection of fuzzy sets describing characteristics of amplitude of ITS, the expression and reconstruction mechanism of ITS and the emergence of model of ITS based on artificial neural network (ANN). The resulting model of ITS not only supports the linguistic output but also the numeric output in interval format. A series of experimental studies is reported for two publicly available financial datasets showing different dynamic characteristics. Experimental results clearly show that the constructed ITS model has the better performance on the linguistic and numeric level.

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