
-
Previous Article
Fault estimation and optimization for uncertain disturbed singularly perturbed systems with time-delay
- NACO Home
- This Issue
-
Next Article
Sliding mode control for uncertain T-S fuzzy systems with input and state delays
An improved ARMA(1, 1) type fuzzy time series applied in predicting disordering
1. | Department of Basic, Shenyang University of Technology, Liaoyang, 111000, China, Department of Mathematics, Northeastern University, Shenyang, 110004, China |
2. | Department of Mathematics, Northeastern University, Shenyang, 110004, China |
Fuzzy time series shows great advantages in dealing with incomplete or unreasonable data. But most of them are based on fuzzy AR time series model, so it is necessary to add MA variables to the fuzzy time series [
References:
[1] |
I. Abdullah, D. Daw and K. Seman,
Traffic forecasting and planning of wimax under multiple priority applying fuzzy time series analysis, J. Appl. Math. Phys., 3 (2015), 68-74.
|
[2] |
G. Box and G. Jenkins,
Time series analysis: forecasting and control, Journal of the Operational Research Society, 37 (1976), 238-242.
|
[3] |
M. Chen and B. Chen,
A hybrid fuzzy time series model based on granular computing for stock price forecasting, Information Sciences, 294 (2015), 227-241.
doi: 10.1016/j.ins.2014.09.038. |
[4] |
S. Chen,
Forecasting enrollments based on fuzzy time series, Fuzzy Sets and Systems, 81 (1996), 311-319.
doi: 10.1016/S0165-0114(98)00266-8. |
[5] |
S. Chen,
Forecasting enrollments based on high-order fuzzy time series, Cybernetics and Systems: An International Journal, 33 (2002), 1-16.
|
[6] |
C. Cheng, T. Chen and C. Chiang, Trend-weighted fuzzy time-series model for taiex forecasting, in Int. Conf. Neural. Hong Kong: Inf. Process, 2006. |
[7] |
H. Y. Guo, P. Witold and X. D. Liu,
Fuzzy time series forecasting based on axiomatic fuzzy set theory, Neural Comput. Appl., 26 (2018), 2807-2817.
|
[8] |
J. R. Hwang, S. M. Chen and C. H. Lee,
Handling forecasting problems using fuzzy time series, Fuzzy Sets and Systems, 100 (1998), 217-228.
doi: 10.1016/S0165-0114(98)00266-8. |
[9] |
T. Jilani, S. Burney and C. Ardil,
Fuzzy metric approach for fuzzy time series forecasting based on frequency density based partitioning, Int. J. Comput. Intell., 4 (2008), 112-117.
|
[10] |
C. Kocak,
Arma(p, q) type high order fuzzy time series forecast method based on fuzzy logic relations, Applied Soft Computing, 58 (2017), 92-103.
|
[11] |
W. Lee and J. Hong,
A hybrid dynamic and fuzzy time series model for mid-term power load forecasting, Int. J. Electr. Power Energy Syst., 64 (2015), 1057-1062.
|
[12] |
B. S. Lian, Q. L. Zhang and J. N. Li,
Sliding mode control for non-linear networked control systems subject to packet disordering via prediction method, Int. Control Theory and Applications, 11 (2017), 3079-3088.
doi: 10.1049/iet-cta.2016.1591. |
[13] |
W. Lu, X. Y. Chen, W. Pedrycz, X. D. Liu and J. H. Yang, Using interval information granules to improve forecasting in fuzzy time series, Int. J. Approx. Reason, 57. (2015), 1–18. |
[14] |
N. Rahman, M. Lee and M. Latif,
Artificial neural networks and fuzzy time series forecasting: an application to air quality, Quality and Quantity, 49 (2015), 2633-2647.
|
[15] |
P. Saxena, K. Sharma and S. Easo,
Forecasting enrollments based on fuzzy time series with higher forecast accuracy rate, Int. J. Comput. Technol., 3 (2012), 95-961.
doi: 10.1002/for.1185. |
[16] |
P. Singh,
High-order fuzzy-neuro-entropy integration-based expert system for time series, Neural Computing and Applications, 28 (2016), 3851-3868.
|
[17] |
Q. Song and B. Chissom,
Forecasting enrollments with fuzzy time series - part Ⅰ, Fuzzy Sets and Systems, 54 (1993), 1-9.
doi: 10.1016/0165-0114(93)90372-O. |
[18] |
T. H. K. Yu,
Weighted fuzzy time series models for TAIEX forecasting, Physica A: Statistical Mechanics and Its Applications, 349 (2005), 609-624.
|
[19] |
G. Yule,
On a method of investigating periodicities in disturbed series with special reference to wolfer's sunspot numbers, Philosophical Transactions of the Royal Society of London, 226 (1927), 26-298.
|
[20] |
L. Zadeh,
Fuzzy sets, Information and Control, 8 (1965), 338-353.
|
show all references
References:
[1] |
I. Abdullah, D. Daw and K. Seman,
Traffic forecasting and planning of wimax under multiple priority applying fuzzy time series analysis, J. Appl. Math. Phys., 3 (2015), 68-74.
|
[2] |
G. Box and G. Jenkins,
Time series analysis: forecasting and control, Journal of the Operational Research Society, 37 (1976), 238-242.
|
[3] |
M. Chen and B. Chen,
A hybrid fuzzy time series model based on granular computing for stock price forecasting, Information Sciences, 294 (2015), 227-241.
doi: 10.1016/j.ins.2014.09.038. |
[4] |
S. Chen,
Forecasting enrollments based on fuzzy time series, Fuzzy Sets and Systems, 81 (1996), 311-319.
doi: 10.1016/S0165-0114(98)00266-8. |
[5] |
S. Chen,
Forecasting enrollments based on high-order fuzzy time series, Cybernetics and Systems: An International Journal, 33 (2002), 1-16.
|
[6] |
C. Cheng, T. Chen and C. Chiang, Trend-weighted fuzzy time-series model for taiex forecasting, in Int. Conf. Neural. Hong Kong: Inf. Process, 2006. |
[7] |
H. Y. Guo, P. Witold and X. D. Liu,
Fuzzy time series forecasting based on axiomatic fuzzy set theory, Neural Comput. Appl., 26 (2018), 2807-2817.
|
[8] |
J. R. Hwang, S. M. Chen and C. H. Lee,
Handling forecasting problems using fuzzy time series, Fuzzy Sets and Systems, 100 (1998), 217-228.
doi: 10.1016/S0165-0114(98)00266-8. |
[9] |
T. Jilani, S. Burney and C. Ardil,
Fuzzy metric approach for fuzzy time series forecasting based on frequency density based partitioning, Int. J. Comput. Intell., 4 (2008), 112-117.
|
[10] |
C. Kocak,
Arma(p, q) type high order fuzzy time series forecast method based on fuzzy logic relations, Applied Soft Computing, 58 (2017), 92-103.
|
[11] |
W. Lee and J. Hong,
A hybrid dynamic and fuzzy time series model for mid-term power load forecasting, Int. J. Electr. Power Energy Syst., 64 (2015), 1057-1062.
|
[12] |
B. S. Lian, Q. L. Zhang and J. N. Li,
Sliding mode control for non-linear networked control systems subject to packet disordering via prediction method, Int. Control Theory and Applications, 11 (2017), 3079-3088.
doi: 10.1049/iet-cta.2016.1591. |
[13] |
W. Lu, X. Y. Chen, W. Pedrycz, X. D. Liu and J. H. Yang, Using interval information granules to improve forecasting in fuzzy time series, Int. J. Approx. Reason, 57. (2015), 1–18. |
[14] |
N. Rahman, M. Lee and M. Latif,
Artificial neural networks and fuzzy time series forecasting: an application to air quality, Quality and Quantity, 49 (2015), 2633-2647.
|
[15] |
P. Saxena, K. Sharma and S. Easo,
Forecasting enrollments based on fuzzy time series with higher forecast accuracy rate, Int. J. Comput. Technol., 3 (2012), 95-961.
doi: 10.1002/for.1185. |
[16] |
P. Singh,
High-order fuzzy-neuro-entropy integration-based expert system for time series, Neural Computing and Applications, 28 (2016), 3851-3868.
|
[17] |
Q. Song and B. Chissom,
Forecasting enrollments with fuzzy time series - part Ⅰ, Fuzzy Sets and Systems, 54 (1993), 1-9.
doi: 10.1016/0165-0114(93)90372-O. |
[18] |
T. H. K. Yu,
Weighted fuzzy time series models for TAIEX forecasting, Physica A: Statistical Mechanics and Its Applications, 349 (2005), 609-624.
|
[19] |
G. Yule,
On a method of investigating periodicities in disturbed series with special reference to wolfer's sunspot numbers, Philosophical Transactions of the Royal Society of London, 226 (1927), 26-298.
|
[20] |
L. Zadeh,
Fuzzy sets, Information and Control, 8 (1965), 338-353.
|



Time | FLR | Weight |
1 | ||
1 | ||
1 | ||
1 | ||
1 |
Time | FLR | Weight |
1 | ||
1 | ||
1 | ||
1 | ||
1 |
Year | Actual price | The rate of change | PRC | Predicted |
(in rupee) | values | |||
6/5/2012 | 2080.25 | |||
6/6/2012 | 2159.45 | 3.81 | 3.81 | |
6/7/2012 | 2167.85 | 0.39 | 0.38 | 2167.66 |
6/8/2012 | 2180.05 | 0.56 | 0.61 | 2181.07 |
6/11/2012 | 2164.55 | -0.71 | -0.65 | 2165.88 |
6/12/2012 | 2206.90 | 1.96 | 1.91 | 2205.89 |
6/13/2012 | 2222.25 | 0.70 | 0.68 | 2221.91 |
6/14/2012 | 2154.25 | -3.06 | -3.12 | 2152.92 |
6/15/2012 | 2182.80 | 1.33 | 0.99 | 2175.58 |
6/18/2012 | 2087.65 | -4.36 | -4.36 | 2087.63 |
⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
7/17/2012 | 2198.85 | 0.20 | 0.16 | 2197.96 |
7/18/2012 | 2185.95 | -0.57 | -0.65 | 2184.56 |
7/19/2012 | 2157.75 | -1.29 | -1.29 | 2157.75 |
7/20/2012 | 2134.55 | -1.08 | -1.03 | 2135.53 |
7/23/2012 | 2092.55 | -1.97 | -1.96 | 2092.71 |
7/24/2012 | 2094.80 | 0.11 | 0.09 | 2094.43 |
7/25/2012 | 2070.65 | -1.15 | -1.03 | 2073.22 |
7/26/2012 | 2017.15 | -2.58 | -2.40 | 2020.95 |
7/27/2012 | 1941.20 | -3.77 | -3.74 | 1941.71 |
7/31/2012 | 2005.20 | 3.30 | 3.36 | 2006.42 |
Year | Actual price | The rate of change | PRC | Predicted |
(in rupee) | values | |||
6/5/2012 | 2080.25 | |||
6/6/2012 | 2159.45 | 3.81 | 3.81 | |
6/7/2012 | 2167.85 | 0.39 | 0.38 | 2167.66 |
6/8/2012 | 2180.05 | 0.56 | 0.61 | 2181.07 |
6/11/2012 | 2164.55 | -0.71 | -0.65 | 2165.88 |
6/12/2012 | 2206.90 | 1.96 | 1.91 | 2205.89 |
6/13/2012 | 2222.25 | 0.70 | 0.68 | 2221.91 |
6/14/2012 | 2154.25 | -3.06 | -3.12 | 2152.92 |
6/15/2012 | 2182.80 | 1.33 | 0.99 | 2175.58 |
6/18/2012 | 2087.65 | -4.36 | -4.36 | 2087.63 |
⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
7/17/2012 | 2198.85 | 0.20 | 0.16 | 2197.96 |
7/18/2012 | 2185.95 | -0.57 | -0.65 | 2184.56 |
7/19/2012 | 2157.75 | -1.29 | -1.29 | 2157.75 |
7/20/2012 | 2134.55 | -1.08 | -1.03 | 2135.53 |
7/23/2012 | 2092.55 | -1.97 | -1.96 | 2092.71 |
7/24/2012 | 2094.80 | 0.11 | 0.09 | 2094.43 |
7/25/2012 | 2070.65 | -1.15 | -1.03 | 2073.22 |
7/26/2012 | 2017.15 | -2.58 | -2.40 | 2020.95 |
7/27/2012 | 1941.20 | -3.77 | -3.74 | 1941.71 |
7/31/2012 | 2005.20 | 3.30 | 3.36 | 2006.42 |
[1] |
Chuang Peng. Minimum degrees of polynomial models on time series. Conference Publications, 2005, 2005 (Special) : 720-729. doi: 10.3934/proc.2005.2005.720 |
[2] |
Ruiqi Li, Yifan Chen, Xiang Zhao, Yanli Hu, Weidong Xiao. Time series based urban air quality predication. Big Data & Information Analytics, 2016, 1 (2&3) : 171-183. doi: 10.3934/bdia.2016003 |
[3] |
Yu-Ting Lin, John Malik, Hau-Tieng Wu. Wave-shape oscillatory model for nonstationary periodic time series analysis. Foundations of Data Science, 2021, 3 (2) : 99-131. doi: 10.3934/fods.2021009 |
[4] |
Antonella Falini, Francesca Mazzia, Cristiano Tamborrino. Spline based Hermite quasi-interpolation for univariate time series. Discrete and Continuous Dynamical Systems - S, 2022 doi: 10.3934/dcdss.2022039 |
[5] |
Yung Chung Wang, Jenn Shing Wang, Fu Hsiang Tsai. Analysis of discrete-time space priority queue with fuzzy threshold. Journal of Industrial and Management Optimization, 2009, 5 (3) : 467-479. doi: 10.3934/jimo.2009.5.467 |
[6] |
Ahmad Deeb, A. Hamdouni, Dina Razafindralandy. Comparison between Borel-Padé summation and factorial series, as time integration methods. Discrete and Continuous Dynamical Systems - S, 2016, 9 (2) : 393-408. doi: 10.3934/dcdss.2016003 |
[7] |
Cheng Peng, Zhaohui Tang, Weihua Gui, Qing Chen, Jing He. A bidirectional weighted boundary distance algorithm for time series similarity computation based on optimized sliding window size. Journal of Industrial and Management Optimization, 2021, 17 (1) : 205-220. doi: 10.3934/jimo.2019107 |
[8] |
Hassan Khodaiemehr, Dariush Kiani. High-rate space-time block codes from twisted Laurent series rings. Advances in Mathematics of Communications, 2015, 9 (3) : 255-275. doi: 10.3934/amc.2015.9.255 |
[9] |
Annalisa Pascarella, Alberto Sorrentino, Cristina Campi, Michele Piana. Particle filtering, beamforming and multiple signal classification for the analysis of magnetoencephalography time series: a comparison of algorithms. Inverse Problems and Imaging, 2010, 4 (1) : 169-190. doi: 10.3934/ipi.2010.4.169 |
[10] |
Editorial Office. Retraction: Xiao-Qian Jiang and Lun-Chuan Zhang, Stock price fluctuation prediction method based on time series analysis. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 915-915. doi: 10.3934/dcdss.2019061 |
[11] |
Hongbiao Fan, Jun-E Feng, Min Meng. Piecewise observers of rectangular discrete fuzzy descriptor systems with multiple time-varying delays. Journal of Industrial and Management Optimization, 2016, 12 (4) : 1535-1556. doi: 10.3934/jimo.2016.12.1535 |
[12] |
Chao Wang, Zhien Li, Ravi P. Agarwal. Hyers-Ulam-Rassias stability of high-dimensional quaternion impulsive fuzzy dynamic equations on time scales. Discrete and Continuous Dynamical Systems - S, 2022, 15 (2) : 359-386. doi: 10.3934/dcdss.2021041 |
[13] |
Xiang Dong, Chengcheng Ren, Shuping He, Long Cheng, Shuo Wang. Finite-time sliding mode control for UVMS via T-S fuzzy approach. Discrete and Continuous Dynamical Systems - S, 2022, 15 (7) : 1699-1712. doi: 10.3934/dcdss.2021167 |
[14] |
Armengol Gasull, Francesc Mañosas. Subseries and signed series. Communications on Pure and Applied Analysis, 2019, 18 (1) : 479-492. doi: 10.3934/cpaa.2019024 |
[15] |
Bernard Ducomet. Asymptotics for 1D flows with time-dependent external fields. Conference Publications, 2007, 2007 (Special) : 323-333. doi: 10.3934/proc.2007.2007.323 |
[16] |
Yuusuke Sugiyama. Degeneracy in finite time of 1D quasilinear wave equations Ⅱ. Evolution Equations and Control Theory, 2017, 6 (4) : 615-628. doi: 10.3934/eect.2017031 |
[17] |
Omid S. Fard, Javad Soolaki, Delfim F. M. Torres. A necessary condition of Pontryagin type for fuzzy fractional optimal control problems. Discrete and Continuous Dynamical Systems - S, 2018, 11 (1) : 59-76. doi: 10.3934/dcdss.2018004 |
[18] |
Jiaquan Zhan, Fanyong Meng. Cores and optimal fuzzy communication structures of fuzzy games. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1187-1198. doi: 10.3934/dcdss.2019082 |
[19] |
Xiaodong Liu, Wanquan Liu. The framework of axiomatics fuzzy sets based fuzzy classifiers. Journal of Industrial and Management Optimization, 2008, 4 (3) : 581-609. doi: 10.3934/jimo.2008.4.581 |
[20] |
Juan J. Nieto, M. Victoria Otero-Espinar, Rosana Rodríguez-López. Dynamics of the fuzzy logistic family. Discrete and Continuous Dynamical Systems - B, 2010, 14 (2) : 699-717. doi: 10.3934/dcdsb.2010.14.699 |
Impact Factor:
Tools
Metrics
Other articles
by authors
[Back to Top]