Partitions based computational method for high-order fuzzy time series forecasting

In this paper, we present a computational method of forecasting based on multiple partitioning and higher order fuzzy time series. The developed computational method provides a better approach to enhance the accuracy in forecasted values. The objective of the present study is to establish the fuzzy logical relations of different order for each forecast. Robustness of the proposed method is also examined in case of external perturbation that causes the fluctuations in time series data. The general suitability of the developed model has been tested by implementing it in forecasting of student enrollments at University of Alabama. Further it has also been implemented in the forecasting the market price of share of State Bank of India (SBI) at Bombay Stock Exchange (BSE), India. In order to show the superiority of the proposed model over few existing models, the results obtained have been compared in terms of mean square and average forecasting errors.

► We propose partitions based computational method of fuzzy time series forecasting.
► Ratio of sum and difference of max and min of data decides number of partitions.
► Method is applied to enrollments at University of Alabama and State Bank of India.
► Accuracy of the method is measured by mean square and average forecasting errors.
► Robustness is tested by taking different cases of fluctuations in time series data.