Prediction of satellite clock errors based on deterministic intuitionistic fuzzy time series

2016 ◽  
Author(s):  
Hu Denghua ◽  
Liu Zan ◽  
Chen Xihong ◽  
Wang Jie
Keyword(s):  
Time Series ◽  
Fuzzy Time Series ◽  
Satellite Clock ◽  
Intuitionistic Fuzzy ◽  
Satellite Clock Errors

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series

2018 ◽  
Vol 14 (01) ◽  
pp. 91-111 ◽  
Author(s):  
Abhishekh ◽  
Surendra Singh Gautam ◽  
S. R. Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stock Exchange ◽  
Market Price ◽  
Intuitionistic Fuzzy Set ◽  
Score Function ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy

Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max–min compositions operator of intuitionistic fuzzy sets to compute the relational matrix [Formula: see text]. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results.

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.

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.

Multi-factor high-order intuitionistic fuzzy time series forecasting model

2016 ◽  
Vol 27 (5) ◽  
pp. 1054-1062 ◽  
Author(s):  
Ya'nan Wang ◽  
◽  
Yingjie Lei ◽  
Yang Lei ◽  
Xiaoshi Fan
Keyword(s):  
Time Series ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Intuitionistic Fuzzy
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