Hesitant fuzzy set based computational method for financial time series forecasting

Non-stochastic hesitation in fuzzy time series forecasting methods occurs due to availability of more than one fuzzification methods of time series data. Recently hesitant fuzzy set has gained attention of the researchers to address issue of aforesaid non-stochastic hesitation. In this research paper, we propose and develop a computational algorithm-based method for financial time series forecasting using hesitant fuzzy set. The proposed method uses hesitant fuzzy logical relations that are constructed using triangular fuzzy sets with equal and unequal intervals. In the proposed forecasting method, hesitant fuzzy logical relations are aggregated using a hesitant fuzzy aggregation operator. Advantages of developed hesitant fuzzy set based forecasting method are that it is easy to implement, can cope with huge time series database and enhances the accuracy in financial time series forecast. To see the performance of proposed method in financial time series forecasting, it is implemented on two financial experimental dataset of TAIFEX and SBI share price at BSE. Rigorous comparison analysis of the proposed forecasting method is done by comparing it with other conventional and computational fuzzy time series forecasting methods in terms of RMSE, AFER. Significance of accuracy enhancement in forecasted outputs is also verified using two-tailed t test.