Extracting fuzzy relations in fuzzy time series model based on approximation concepts
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. (C) 2011 Elsevier Ltd. All rights reserved.
fuzzy time series rough set theory data mining fuzzy rough time series approximation reasoning