TY - JOUR
T1 - Markov frameworks and stock market decision making
AU - Koppula, Kavitha
AU - Kedukodi, Babushri Srinivas
AU - Kuncham, Syam Prasad
PY - 2020/11/1
Y1 - 2020/11/1
N2 - In this paper, we present applications of Markov rough approximation framework (MRAF). The concept of MRAF is defined based on rough sets and Markov chains. MRAF is used to obtain the probability distribution function of various reference points in a rough approximation framework. We consider a set to be approximated together with its dynamacity and the effect of dynamacity on rough approximations is stated with the help of Markov chains. An extension to Pawlak’s decision algorithm is presented, and it is used for predictions in a stock market environment. In addition, suitability of the algorithm is illustrated in a multi-criteria medical diagnosis problem. Finally, the definition of fuzzy tolerance relation is extended to higher dimensions using reference points and basic results are established.
AB - In this paper, we present applications of Markov rough approximation framework (MRAF). The concept of MRAF is defined based on rough sets and Markov chains. MRAF is used to obtain the probability distribution function of various reference points in a rough approximation framework. We consider a set to be approximated together with its dynamacity and the effect of dynamacity on rough approximations is stated with the help of Markov chains. An extension to Pawlak’s decision algorithm is presented, and it is used for predictions in a stock market environment. In addition, suitability of the algorithm is illustrated in a multi-criteria medical diagnosis problem. Finally, the definition of fuzzy tolerance relation is extended to higher dimensions using reference points and basic results are established.
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U2 - 10.1007/s00500-020-04950-4
DO - 10.1007/s00500-020-04950-4
M3 - Article
AN - SCOPUS:85085329756
SN - 1432-7643
JO - Soft Computing
JF - Soft Computing
ER -