A FCM-based Domain Partition Method for Time Series Data Set

doi:10.3969/j.issn.1006-2475.2015.05.002

Abstract

Abstract: With the development of society, people increasingly demand for data prediction, fuzzy time series has been attracted much attention because it can handle the ambiguities in time series data. To improve the prediction accuracy of the model, domain partition is crucial as the first step of the prediction. In this paper, we present a FCM-based two-time domain partition method. We make the first partition according to the clustering center obtained from FCM clustering, the second partition to realize unequal domain according to the density distribution of the sample points. Finally, the feasibility of the new method is verified through the prediction of classic sample.

Key words: time series, domain partition, FCM clustering algorithm, data forecast

CLC Number:

TP391

LIU Che, LIU Xuan, MA Hong-xu. A FCM-based Domain Partition Method for Time Series Data Set[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.05.002.

References

[1] Song Qiang, Chissom B S. Fuzzy time series and its models[J]. Fuzzy Sets and Systems, 1993,54(3):269-277.

[2] Chen Shyi-Ming. Forecasting enrollments based on fuzzy time series[J]. Fuzzy Sets and Systems, 1996,81(3):311-319.

[3] Hwang Jeng-Ren, Chen Shyi-Ming, Lee Chia-Hoang. Handling forecasting problems using fuzzy time series[J]. Fuzzy Sets and Systems, 1998,100(1-3):217-228.

[4] Jilani T A, Burney S M A. A refined fuzzy time series model for stock market forecasting[J]. Physica A: Statistical Mechanics and its Applications, 2008,387(12):2857-2862.

[5] Yu Hui-Kuang. A refined fuzzy time-series model for forecasting[J]. Physica A: Statistical Mechanics and its Applications, 2005,346(3-4):657-681.

[6] Aladag C H, Basaran M A, Egrioglu E, et al. Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations[J]. Expert Systems with Applications, 2009,36(3):4228-4231.

[7] Aladag C H, Yolcu U, Egrioglu E. A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks[J]. Mathematics and Computers in Simulation, 2010,81(4):875-882.

[8] Egrioglu E, Aladag C H, Yolcu U, et al. Finding an optimal interval length in high order fuzzy time series[J]. Expert Systems with Applications, 2010,37(7):5052-5055.

[9] Yolcu U, Egrioglu E, Uslu V R, et al. A new approach for determining the length of intervals for fuzzy time series[J]. Applied Soft Computing, 2009,9(2):647-651.

[10] Egrioglu E, Aladag C H, Yolcu U, et al. A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model[J]. Expert Systems with Applications, 2009,36(4):7424-7434.

[11] Egrioglu E, Aladag C H, Yolcu U, et al. Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering[J]. Expert Systems with Applications, 2011,38(8):10355-10357.

[12] 余文利,方建文,廖建平. 一种新的基于模糊C均值算法的模糊时间序列确定性预测模型[J]. 计算机工程与科学, 2010,32(7):112-116.

[13] Li Sheng-Tun, Cheng Yi-Chung, Lin Su-Yu. A FCM-based deterministic forecasting model for fuzzy time series[J]. Computers & Mathematics with Applications, 2008,56(12):3052-3063.

[14] 赵庆江,迟凯,付芳萍,等. 基于FCM的模糊时间序列模型及人民币汇率预测[C]// 第二十九届中国控制会议论文集. 2010:5526-5529.

[15] Cheng Ching-Hsue, Chang Jing-Rong, Yeh Che-An. Entropy-based and trapezoid fuzzification-based fuzzy time series approaches for forecasting IT project cost[J]. Technological Forecasting and Social Change, 2006,73(5):524-542.

[16] Chen Tai-Liang, Cheng Ching-Hsue, Teoh H J. Fuzzy time-series based on Fibonacci sequence for stock price forecasting[J]. Physica A: Statistical Mechanics and its Applications, 2007,380:377-390.

[17] Cheng Ching-Hsue, Chen Tai-Liang, Wei Liang-Ying. A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting[J]. Information Sciences, 2010,180(9):1610-1629.