IFS and SODA based computational method for fuzzy time series forecasting

Time series forecasting has fascinated a great deal of interest from various research communities due to its wide applications in medicine, economics, finance, engineering and many other crucial fields. Various studies in past have shown that intuitionistic fuzzy sets (IFSs) not only handle non-stochastic non-determinism in time series forecasting but also enhance accuracy in forecasted outputs. Clustering is another one of the methods that im-proves accuracy of time series forecasting. The contribution of this research work is a novel computational fuzzy time series (FTS) forecasting method which relies on IFSs and self-organized direction aware (SODA) approach of clustering. The usage of SODA aids in making the proposed FTS forecasting method as autonomous as feasible, as it does not require human intervention or prior knowledge of the data. Forecasted outputs in proposed FTS forecasting method are computed using a weighted formula and weights are optimized using grey wolf opti-mization (GWO) method. Proposed FTS is applied to forecast enrolments of the University of Alabama and market price of State Bank of India (SBI) share at Bombay stock exchange (BSE), India and performance is compared in terms of root mean square error (RMSE), average forecasting error (AFE) and mean absolute de-viation (MAD). Goodness of the proposed FTS forecasting method in forecasting enrolments of the University of Alabama and market price of SBI share is also tested using coefficient of correlation and determination, criteria of Akaike and Bayesian information.