Fuzzy time series and its models
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II
Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Handling forecasting problems using fuzzy time series
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Fuzzy relation analysis in fuzzy time series model
Computers & Mathematics with Applications
Temperature prediction using fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ratio-based lengths of intervals to improve fuzzy time series forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The deriving of fuzzy relationships is an essential task in fuzzy time-series forecasting studies; many studies have been devoted to discovering fuzzy relationships using less computational effort. In this paper, we also aim to improve the derivation of fuzzy relationships, and compare the results to previous studies. The proposed model in this paper not only requires no prior knowledge or pre-review dataset to generate heuristic rules, but also effectively reduces computational effort by decreasing the quantity of fuzzy sets of linguistic variables. The rough set classifier is introduced to discover fuzzy relationships first when a time-invariant relation is derived. The empirical results show that the proposed model's MSE (mean square error) is 79,040, the MAPE (Mean absolute percentage error) is 1.47% and the time complexity outperforms previous models and yields the best known result.