A Computational Method of Forecasting Based on Intuitionistic Fuzzy Sets and Fuzzy Time Series

2012 ◽  
pp. 993-1000 ◽  
Author(s):  
Bhagawati P. Joshi ◽  
Sanjay Kumar
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Intuitionistic Fuzzy Sets ◽  
Computational Method ◽  
Fuzzy Time Series ◽  
Intuitionistic Fuzzy

Fuzzy Time Series Model Based on Intuitionistic Fuzzy Sets for Empirical Research in Stock Market

2012 ◽  
Vol 3 (4) ◽  
pp. 71-84 ◽  
Author(s):  
Bhagawati P. Joshi ◽  
Sanjay Kumar
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Stock Exchange ◽  
Intuitionistic Fuzzy Set ◽  
Intuitionistic Fuzzy Sets ◽  
Computational Approach ◽  
Computational Method ◽  
Fuzzy Time Series ◽  
Market Prices ◽  
Intuitionistic Fuzzy

Intuitionistic fuzzy sets introduced by Atanassov are generalization of fuzzy sets as they also handle the non-determinacy which is caused by degree of hesitation of decision maker. The present study proposes a computational method of forecasting for fuzzy time series. In the proposed method the notion of intuitionistic fuzzy set is used in fuzzy time series forecasting with simplified computational approach. The developed model has been tested on the movement of share market prices of State Bank of India (SBI) at Bombay Stock Exchange (BSE), India. Further the method has been implemented for forecasting SENSEX of BSE. The suitability of the developed model has also been examined by comparing it with the other existing models to show its superiority.

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.

Intuitionistic Fuzzy Time Series Forecasting Based on Dual Hesitant Fuzzy Set for Stock Market

2019 ◽  
pp. 37-57
Author(s):  
Sanjay Kumar ◽  
Kamlesh Bisht ◽  
Krishna Kumar Gupta
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Time Series Data ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Hesitant Fuzzy Set ◽  
Series Data ◽  
Intuitionistic Fuzzy ◽  
Dual Hesitant Fuzzy Set

In this chapter, an application of dual hesitant fuzzy set (DHFS) in intuitionistic fuzzy time series forecasting is proposed to handle fuzziness and non-determinism that occurs due to multiple valid fuzzification method for time series data. Advantages of the proposed DHFS-based time series forecasting method are that it includes characteristics of both intuitionistic and hesitant fuzzy sets to handle the non-determinism and hesitancy corresponding to single membership grade multiple membership grades of an element. In the present study, universe of discourse is partitioned and fuzzified the time series data by two different fuzzification methods (triangular and Gaussian) to construct DHFS. Further, elements of DHFS are aggregated to construct the intuitionistic fuzzy sets. Proposed method is implemented over the share market prizes of SBI at BSE, India and SENSEX of BSE to confirm its out performance over existing time series forecasting methods using RMSE and AFER.

scholarly journals A Unique Computational Method for Constructing Intervals in Fuzzy Time Series Forecasting

2018 ◽  
Vol 18 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Shilpa Jain ◽  
Prakash C. Mathpal ◽  
Dinesh Bisht ◽  
Phool Singh
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Expert Knowledge ◽  
Critical Issue ◽  
Time Series Forecasting ◽  
Computational Method ◽  
Fuzzy Time Series ◽  
University Of Alabama ◽  
Mean And Variance ◽  
Authentic Data

Abstract This research article suggests a computational method for constructing fuzzy sets in absence of expert knowledge. This method uses concepts of central tendencies mean and variance. This study gives a solution to the critical issue in designing of fuzzy systems, number of fuzzy sets. Proposed computational method helps in finding intervals and thereby fuzzy sets for fuzzy time series forecasting. Proposed computational method is implemented on the authentic data for the enrolments of University of Alabama, which is considered as benchmark problem in the field of fuzzy time series. The forecasted values are compared with the results of other methods to state its supremacy. Projected computational method along with Gaussian membership function gave promising results over other methods for fuzzy time series for the above said benchmark data.

scholarly journals Sugeno Intuitionistic Fuzzy Generator Based Computational Technique for Crude Oil Price Forecasting

2020 ◽  
Vol 5 (3) ◽  
pp. 488-496
Author(s):  
Gunjan Goyal ◽  
Dinesh C. S. Bisht
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Fuzzy Sets ◽  
Time Series Forecasting ◽  
Oil Price ◽  
Fuzzy Time Series ◽  
Spot Price ◽  
Computational Technique ◽  
Crude Oil Price ◽  
Intuitionistic Fuzzy

Crude oil being a significant source of energy, change of crude oil price can affect the global economy. In this paper, a new approach based on the intuitionistic fuzzy set theory has been implemented to predict the crude oil price. This paper presents the intuitionistic fuzzy time series forecasting algorithm to enhance the efficacy of time series forecasting which includes fuzzy c-means clustering to obtain the optimal cluster centers. Further, a computational technique is proposed for the construction of triangular fuzzy sets and these fuzzy sets are converted to intuitionistic fuzzy sets with the help of Sugeno type intuitionistic fuzzy generator. The popular benchmark dataset of West Texas Intermediate crude oil spot price is used for the validation process. The numerical results when compared with existing methods notify that the proposed method enhances the accuracy of the crude oil price forecasts.

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