A Forecasting Method of Fuzzy Time Series for Imbalanced Data

doi:10.3969/j.issn.1006-2475.2017.12.021

Abstract

Abstract: Fuzzy time series has been widely concerned since it was put forward. It is the most important to increase accuracy of prediction when using fuzzy time series to forecast. In order to make the forecast more accurate, firstly imbalanced data are classified, then prediction is made by fuzzy time series. At last the method is shown its practicability by experiments.

Key words: imbalanced data, fuzzy time series, universe of discourse be divided, fuzzy c-means

CLC Number:

O244
TP311

HUO Xu1, WU Tao 1,2. A Forecasting Method of Fuzzy Time Series for Imbalanced Data[J]. Computer and Modernization, 2017, 0(12): 108-110.

References

［1］Song Q, Chissom B S. Forecasting enrollments with fuzzy time series-PartⅠ［J］. Fuzzy Sets and Systems, 1993,54(1):1-9. ［2］Tsaur R C， Kuo T C． The adaptive fuzzy time series model with an application to Taiwan’s tourism demand［J］． Expert Systems with Applications， 2011,38(8):9164-9171. ［3］杨一文,蔺玉佩. 模糊时间序列建模及股票市场多步预测［J］. 计算机工程与应用, 2014,50(5):252-256. ［4］陈刚，曲宏巍.一种新的模糊时间序列模型的预测方法［J］. 控制与决策, 2013,28(1):105-108. ［5］Sumit S, Vipul J. Genetic algorithm based fuzzy time series tourism demand forecast model［J］. Industrial Management & Data Systems, 2016,116(3):483-507. ［6］Chen S M, Chung N Y. Forecasting enrollments of students by using fuzzy time series and genetic algorithms［J］. Information and Management Sciences, 2006,17(3):1-17. ［7］Cheng C H, Chang R J, Yeh C A. Entropy-based and trapezoidal fuzzification based fuzzy time series approach for forecasting IT project cost［J］. Technological Forecasting and Social Change, 2006,73(5):524-542. ［8］Huarng K, Yu T H K. The application of neural networks to forecast fuzzy time series［J］. Physica A, 2006,363(2):481-491. ［9］Huarng K. Heuristic models of fuzzy time series forforecasting［J］. Fuzzy Sets and Systems, 2001,123(3):369-386. ［10］王国徽,姚俭. 基于K-means算法的模糊时间序列预测模型［J］. 应用泛函分析学报, 2015,17(1):58-65. ［11］王威娜，阚中勋. 基于模糊C-均值的模糊时间序列模型［J］. 吉林化工学院学报, 2014,31(9):74-76. ［12］Erol E. A new time-invariant fuzzy time series forecasting method based on genetic algorithm［J］. Advances in Fuzzy Systems, 2012,2012(3):2-7. ［13］鲍永胜，吴振升. 基于SVM的时间序列短期风速预测［J］. 中国电力, 2011,44(9):61-64. ［14］董振波. 基于加权模糊聚类的不平衡数据分类方法［J］. 现代计算机(专业版), 2016,2016(17):25-27. ［15］Pal N R， Bezdek J C． On cluster validity for the fuzzy c-means model［J］． IEEE Transactions on Fuzzy Systems, 2002,3(3):370-379. ［16］Hathaway R J， Bezdek J C. Fuzzy c-means clustering of incomplete data［J］. IEEE Transactions on Systems Man & Cybernetics, Part B Cybernetics, 2001,31(5):735-744. ［17］Galar M， Fernandez A， Barrenechea E, et al．A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches［J］. IEEE Transactions on Systems Man & Cybernetics, Part C Applications & Reviews, 2012,42(2):463-484.

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