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Intuitionistic Fuzzy Time Series Forecasting Based on Dual Hesitant Fuzzy Set for Stock Market: DHFS-Based IFTS Model for Stock Market
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Author(s): Sanjay Kumar (G. B. Pant University of Agriculture and Technology, India), Kamlesh Bisht (G. B. Pant University of Agriculture and Technology, India)and Krishna Kumar Gupta (G. B. Pant University of Agriculture and Technology, India)
Copyright: 2019
Pages: 21
Source title:
Exploring Critical Approaches of Evolutionary Computation
Source Author(s)/Editor(s): Muhammad Sarfraz (Kuwait University, Kuwait)
DOI: 10.4018/978-1-5225-5832-3.ch003
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Abstract
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.
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