A Heuristic Adaptive-order Intuitionistic Fuzzy Time Series Forecasting Model

WANG Yanan; LEI Yingjie; WANG Yi; ZHENG Kouquan

doi:10.11999/JEIT160013

Volume 38 Issue 11

Dec. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(11): 2795-2802

WANG Yanan, LEI Yingjie, WANG Yi, ZHENG Kouquan. A Heuristic Adaptive-order Intuitionistic Fuzzy Time Series Forecasting Model[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2795-2802. doi: 10.11999/JEIT160013

Citation:

WANG Yanan, LEI Yingjie, WANG Yi, ZHENG Kouquan. A Heuristic Adaptive-order Intuitionistic Fuzzy Time Series Forecasting Model[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2795-2802. doi: 10.11999/JEIT160013

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A Heuristic Adaptive-order Intuitionistic Fuzzy Time Series Forecasting Model

doi: 10.11999/JEIT160013

1.
(Air and Missile Defense College, Air Force Engineering University, Xi&rsquo
2.
(Xi&rsquo

Funds:

The National Natural Science Foundation of China (61402517)

Received Date: 2016-01-04
Rev Recd Date: 2016-05-26
Publish Date: 2016-11-19

Abstract

Abstract

Considering that the existing high-order models have limitations in forecast range and accuracy, a heuristic adaptive-order intuitionistic fuzzy time series forecasting model is built with the combination of the intuitionistic fuzzy sets theory. In this model, a direct fuzzy clustering algorithm is used to partition the universe of discourse into unequal intervals. The traditional method of ascertaining the membership and non-membership functions of intuitionistic fuzzy set are also modified to fit the intuitionistic fuzzy time series data. On these basis, variable high-order forecasting rules are established and the prior knowledge of tendency is used in defuzzification to extend the forecasting range. At last, contrast experiments on the enrollments of the University of Alabama and the daily average temperature of Beijing are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.
- Intuitionistic fuzzy set,
- Time series forecast,
- Heuristic,
- Adaptive order

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References(33)

References

SONG Q and CHISSOM B S. Fuzzy time series and its models[J]. Fuzzy Sets and Systems, 1993, 54(1): 269-277. doi: 10.1016/0165-0114(93)90372-O.

SONG Q and CHISSOM B S. Forecasting enrollments with fuzzy time series-Part I[J]. Fuzzy Sets and Systems, 1993, 54(1): 1-9. doi: 10.1016/0165-0114(93)90355-L.

SONG Q and CHISSOM B S. Forecasting enrollments with fuzzy time seriesPart II[J]. Fuzzy Sets and System, 1994, 62(1): 1-8. doi: 10.1016/0165-0114(94)90067-1.

EFENDI R, ISMAIL Z, and DERIS M M. A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand[J]. Applied Soft Computing, 2015, 28(3): 422-430. doi: 10.1016/j.asoc.2014.11.043.

SUN Baiqing, GUO Haifeng, KARIMI H R, et al. Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series[J]. Neurocomputing, 2015, 151(3): 1528-1536. doi: 10.1016/j.neucom.2014.09.018.

TSAUR R C and KUO T C. Tourism demand forecasting using a novel high-precision fuzzy time series model[J]. International Journal of Innovative Computing, Information and Control, 2014, 10(2): 695-701.

HUANG K and YU T H-K. Ratio-based lengths of intervals to improve fuzzy time series forecasting[J]. IEEE Transactions on Systems, Man, and CyberneticsPart B: Cybernetics, 2006, 36(2): 328-340. doi: 10.1109/TSMCB.2005.857093.

CAI Qisen, ZHANG Defu, ZHENG Wei, et al. A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression[J]. Knowledge-Based Systems, 2015, 74(1): 61-68. doi: 10.1016/j.knosys.2014.11.003.

IZAKIAN H, PEDRYCZ W, and JAMAL I. Fuzzy clustering of time series data using dynamic time warping distance[J]. Engineering Applications of Artificial Intelligence, 2015, 39(3): 235-244. doi: 10.1016/j.engappai.2014.12.015.

LU Wei, CHEN Xueyan, PEDRYCZ W, et al. Using interval information granules to improve forecasting in fuzzy time series[J]. International Journal of Approximate Reasoning, 2015, 57(2): 1-18. doi: 10.1016/j.ijar.2014.11.002.

ABDOLLAHZADE M, MIRANIAN A, HASSANI H, et al. A new hybrid enhanced local linear neuro-fuzzy model based on the optimized singular spectrum analysis and its application for nonlinear and chaotic time series forecasting[J]. Information Sciences, 2015, 295(2): 107-125. doi: 10.1016/ j.ins.2014.09.002.

CHENG S H, CHEN S M, and JIAN W S. Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures[J]. Information Sciences, 2016, 327(1): 272-287. doi: 10.1016/j.ins.2015.08.024.

PENG Hungwen, WU Shenfu, WEI Chiaching, et al. Time series forecasting with a neuro-fuzzy modeling scheme[J]. Applied Soft Computing, 2015, 32(7): 481-493. doi: 10.1016/ j.asoc.2015.03.059.

CHEN M Y and CHEN B T. Online fuzzy time series analysis based on entropy discretization and a fast Fourier transform[J]. Applied Soft Computing, 2014, 14(1): 156-166. doi: 10.1016/j.asoc.2013.07.024.

DENG W, WANG G, and ZHANG X. A novel hybrid water quality time series prediction method based on cloud model and fuzzy forecasting[J]. Chemometrics and Intelligent Laboratory Systems, 2015, 149(12): 39-49. doi: 10.1016/ j.chemolab.2015.09.017.

PEREIRA C M, ALMEIDA N N, and VELLOSO M. Fuzzy modeling to forecast an electric load time series[J]. Procedia Computer Science, 2015, 55: 395-404. doi: 10.1016/j.procs. 2015.07.089.

SINGH P and BORAH B. Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization[J]. International Journal of Approximate Reasoning, 2014, 55(3): 812-833. doi: 10.1016/j.ijar.2013. 09.014.

CHEN S M and CHEN S W. Fuzzy forecasting based on two-factor second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships[J]. IEEE Transactions on Cybernetics, 2015, 45(3): 405-417. doi: 10.1109/TCYB.2014.2326888.

HWANG J R, CHEN S M, and LEE C H. Handling forecasting problems using fuzzy time series[J]. Fuzzy Sets and Systems, 1998, 100(1): 217-228. doi: 10.1016/S0165-0114(97)00121-8.

LIU Haotien and WEI Maolen. An improved fuzzy forecasting method for seasonal time series[J]. Expert Systems with Applications, 2010, 37(9): 6310-6318. doi: 10.1016/ j.eswa.2010.02.090.

SINGH P and BORACH B. High-order fuzzy-neuro expert system for time series forecasting[J]. Knowledge-Based Systems, 2013, 46(7): 12-21. doi: 10.1016/j.knosys.2013. 01.030.

ASKARI S and MONTAZERIN N. A high-order multi-variable fuzzy time series forecasting algorithm based on fuzzy clustering[J]. Expert Systems with Applications, 2015, 42(4): 2121-2135. doi: 10.1016/j.eswa.2014.09.036.

CASTILLO O, ALANIS A, GARCIA M, et al. An intuitionistic fuzzy system for time series analysis in plant monitoring and diagnosis[J]. Applied Soft Computing, 2007, 7(4): 1227-1233. doi: 10.1016/j.asoc.2006.01.010.

JOSHI B P and KUMAR S. Intuitionistic fuzzy sets based method for fuzzy time series forecasting[J]. Cybernetics and Systems: An International Journal, 2012, 43(1): 34-47. doi: 10.1080/01969722.2012.637014.

郑寇全, 雷英杰, 王睿, 等. 直觉模糊时间序列建模及应用[J]. 控制与决策, 2013, 28(10): 1525-1530.

ZHENG Kouquan, LEI Yingjie, WANG Rui, et al. Modeling and application of IFTS[J]. Control and Decision, 2013, 28(10): 1525-1530.

郑寇全, 雷英杰, 王睿, 等.参数自适应的长期IFTS预测算法[J]. 系统工程与电子技术, 2014, 36(1): 99-104. doi: 10.3969/ j.issn.1001-506X.2014.01.16.

ZHENG Kouquan, LEI Yingjie, WANG Rui, et al. Method of long-term IFTS forecasting based on parameter adaption[J]. Systems Engineering and Electronics, 2014, 36(1): 99-104. doi: 10.3969/j.issn.1001-506X.2014.01.16.

梁保松, 曹殿立. 模糊数学极其应用[M]. 北京: 科学出版社, 2007: 65-85.

LIANG Baosong and CAO Dianli. Fuzzy Mathematics and Applications[M]. Beijing: Science Press, 2007: 65-85.

雷英杰, 赵杰, 路艳丽, 等. 直觉模糊集理论及应用[M]. 北京: 科学出版社, 2014: 28-189.

LEI Yingjie, ZHAO Jie, LU Yanli, et al. Theories and Applications of Intuitionistic Fuzzy Set[M]. Beijing: Science Press, 2014: 28-189.

WANG Yanan, LEI Yingjie, FAN Xiaoshi, et al. Intuitionistic fuzzy time series forecasting model based on intuitionistic fuzzy reasoning[J]. Mathematical Problems in Engineering, 2016(2016): 1-12. doi: 10.1155/2016/5035160.

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