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

Abstract

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.

Cross-Entropy and Prioritized Aggregation Operator with Simplified Neutrosophic Sets and Their Application in Multi-Criteria Decision-Making Problems

“…Because of the advantages that an IFS considers the membership-degree, non-membership degree and hesitation degree simultaneously, it is more flexible and useful to describe the uncertain information than a traditional FS. Thus, many methods based on IFSs have been put forward and widely applied to solve MCDM problems [6][7][8][9][10][11][12], medical diagnosis [13,14], pattern recognition [15,16], stock market prediction [17,18], and marketing strategy selection [19]. However, in some real situations, the membership degree, non-membership degree and hesitation degree may be difficultly given by specific numbers; hence, interval-valued intuitionistic fuzzy sets (IVIFSs) [20] are developed and applied to solve such problems [21][22][23][24][25].…”

An Improved Weighted Correlation Coefficient Based on Integrated Weight for Interval Neutrosophic Sets and its Application in Multi-criteria Decision-making Problems

“…be an entropy weight vector calculated according to Equation (11). According to Theorem 3, it is known that the entropy value of INSs lies between 0 and 1, i.e.,…”

“…Hesitant fuzzy sets (HFSs) were introduced by Torra and Narukawa 8 to deal with situations where people are hesitant in expressing their preference regarding objects in a decision-making process. Moreover, all these extensions of FSs have been developed by authors working in various fields [9][10][11] with further extensions still being proposed [12][13][14][15][16][17] . In particular, Florentin Smarandache 18,19 introduced neutrosophic logic and neutrosophic sets (NSs) in 1995, with the latter being characterized by the functions of truth, indeterminacy and falsity.…”

New Concepts of Picture Fuzzy Graphs with Application

“…Over the last few decades, the intuitionistic fuzzy set has been obtaining lots of attention from researchers and scientists. Intuitionistic fuzzy set is used in successfully in different field including image processing [3], decision making [4], social network [5], logic programming [6], market prediction [7], machine learning [8], medical diagnosis recognition [9], robotic systems [10], etc. The concept of neutrality degree cannot be considered in intuitionistic fuzzy set theory.…”