Intuitionistic fuzzy sets (IFSs) have been proved to be more ideal than fuzzy sets to handle non-probabilistic uncertainty and nondeterminism in the system. The present study proposes an IFS-based computational method to address the issue of non-determinism in financial time series forecasting. The proposed IFS-based forecasting method uses a simple computational algorithm to forecast without using complex computations using intuitionistic fuzzy logical relations. In order to see suitability of the proposed method in financial forecasting, it is implemented on three experimental data of the SBI share price, TAIEX and Dow Jones Industrial Average (DJIA). Root mean square error and statistical test are used in the study to confirm the out performance of the proposed IFS-based computational method of forecasting. Experimental results show that the proposed method outperforms various existing methods for forecasting SBI, TAIEX and DJIA.
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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