A fuzzy non-parametric time series model based on fuzzy data

[1] S. S. G. Abhishekh, S. R. Singh, A score function-based method of forecasting using intuitionistic fuzzy time series, New Mathematics and Natural Computation, 14(1) (2018), 91-111.

[2] C. H. Aladag, Using multiplicative neuron model to establish fuzzy logic relationships, Expert Systems with Applications, 40(3) (2013), 850-853.

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

[4] L. C. D. Barros, R. C. Bassanezi, W. A. Lodwick, A first course in fuzzy logic, fuzzy dynamical systems and biomathematics: Theory and applications, Springer, Berlin Heidelberg, 2017.

[5] M. Bose, K. Mali, A novel data partitioning and rule selection technique for modeling high-order fuzzy time series, Applied Soft Computing, 63 (2018), 87-96.

[6] G. E. P. Box, G. M. Jenkins, Time series analysis: Forecasting and control, Holden-Day, San Francisco, 1976.

[7] P. J. Brockwell, R. A. Davies, Time series: Theory and methods, Springer-Verlag, New York, 1991.

[8] E. Bulut, Modeling seasonality using the fuzzy integrated logical forecasting (FILF) approach, Expert Systems with Applications, 41(4) (2014), 1806-1812.

[9] L. J. Cao, E. H. T. Francis, Support vector machine with adaptive parameters in financial time series forecasting, IEEE Transactions Neural Netw, 14(6) (2003), 1506-1518.

[10] M. Y. Chen, A high-order fuzzy time series forecasting model for internet stock trading, Future Generation Computer Systems, 37 (2014), 461-467.

[11] M. Y. Chen, B. T. Chen, Online fuzzy time series analysis based on entropy discretization and a fast Fourier transform, Applied Soft Computing, 14 (2014), 156-166.

[12] S. M. Chen, S. W. Chen, Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships, IEEE Transactions on Cybernetics, 45(3) (2015), 405-417.

[13] S. M. Chen, K. Tanuwijaya, Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques, Expert Systems with Applications, 38(3) (2011), 10594-10605.

[14] C. H. Cheng, C. H. Chen, Fuzzy time series model based on weighted association rule for financial market forecasting, Expert Systems, 35 (2018), 23-30.

[15] S. H. Cheng, S. M. Chen, W. S. Jian, Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures, Information Sciences, 327 (2016), 272-287.

[16] O. Duru, E. Bulut, A non-linear clustering method for fuzzy time series: Histogram damping partition under the optimized cluster paradox, Applied Soft Computing, 24 (2014), 742-748.

[17] R. Efendi, Z. Ismail, M. M. Deris, A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand, Applied Soft Computing, 28 (2015), 422-430.

[18] S. Efromovich, Nonparametric curve estimation: Methods, theory and applications, New York, Springer, 1999.

[19] E. Egrioglu, C. H. Aladag, U. Yolcu, Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks, Expert Systems with Applications, 40(3) (2013), 854-857.

[20] S. S. Gautam, S. Singh, A refined method of forecasting based on high-order intuitionistic fuzzy time series data, Progress in Artificial Intelligence, 7 (2018), 339-350.

[21] F. Gaxiola, P. Melin, F. Valdez, O. Castillo, Interval type-2 fuzzy weight adjustment for back propagation neural networks with application in time series prediction, Information Sciences, 260 (2014), 1-14.

[22] H. Guan, Z. Dai, A. Zhao, J. He, A novel stock forecasting model based on high-order-fuzzy-fluctuation trends and back propagation neural network, PLoS ONE 13(2) (2018): e0192366, Doi: 10.1371/journal.
pone.0192366.

[23] C. Gupta, G. Jain, D. K. Tayal, O. Castillo, ClusFuDE: Forecasting low dimensional numerical data using an improved method based on automatic clustering, fuzzy relationships and differential evolution, Engineering Applications of Artificial Intelligence, 71 (2018), 175-189.

[24] G. Hesamian, M. G. Akbari, A semi-parametric model for time series based on fuzzy data, IEEE Transactions on Fuzzy Systems, 26 (2018), 2953-2966.

[25] G. Hesamian, M. G. Akbari, Fuzzy absolute error distance measure based on a generalised difference operation, International Journal of Systems Science, Taylor and Francis Journals, 49(11) (2018), 2454-2462.

[26] G. Hesamian, M. G. Akbari, A fuzzy additive regression model with exact predictors and fuzzy responses, Applied Soft Computing, (2020), Doi: 10.1016/j.asoc.2020.106507.

[27] G. Hesamian, J. Chachi, Two-sample Kolmogorov-Smirnov fuzzy test for fuzzy random variables, Statistical Papers, 56 (2015), 61-82.

[28] Y. L. Huang, S. J. Horng, M. He, P. Fan, T. W. Kao, M. K. Khan, J. L. Lai, I. H. Kuo, A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization, Expert Systems with Applications, 38(7) (2011), 8014-8023.

[29] 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.

[30] R. Li, Water quality forecasting of Haihe River based on improved fuzzy time series model, Desalination and Water Treatment, 106 (2018), 285-291.

[31] S. T. Li, S. C. Kuo, Y. C. Cheng, C. C. Chen, Deterministic vector long-term forecasting for fuzzy time series, Fuzzy Sets and Systems, 161(13) (2010), 1852-1870.

[32] V. Novák, Detection of structural breaks in time series using fuzzy techniques, International Journal of Fuzzy Logic and Intelligent Systems, 18(1) (2018), 1-12.

[33] W. Palma, Time series analysis, Wiley Series in Probability and Statistics, 2016.

[34] H. W. Peng, S. F. Wu, C. C. Wei, S. J. Lee, Time series forecasting with a neuro-fuzzy modeling scheme, Applied Soft Computing, 32 (2015), 481-493.

[35] T. T. H. Phan, A. Bigand, E. P. Caillault, A new fuzzy logic-based similarity measure applied to large gap imputation for uncorrelated multivariate time series, Applied Computational Intelligence and Soft Computing, (2018), 1-15.

[36] N. F. Rahim, M. Othman, R. Sokkalingam, E. A. Kadir, Forecasting crude palm oil prices using fuzzy rule-based time series method, IEEE Access, 6 (2018), 32216-32224.

[37] H. J. Sadaei, R. Enayatifar, A. H. Abdullah, A. Gani, Short-term load forecasting using a hybrid model with a refined exponentially weighted fuzzy time series and an improved harmony search, Electrical Power and Energy Systems, 62 (2014), 118-129.

[38] H. J. Sadaei, R. Enayatifar, F. G. Guimaraes, M. Mahmud, Z. A. Alzamil, Combining ARFIMA models and fuzzy time series for the forecast of long memory time series, Neurocomput, 175 (2016), 782-796.

[39] H. J. Sadaei, R. Enayatifar, M. H. Lee, M. Mahmud, A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting, Applied Soft Computing, 40 (2016), 132-149.

[40] R. H. Shumway, D. S. Stoffer, Time series analysis and its applications, Springer International Publishing, 2011.

[41] P. Singh, B. Borah, High-order fuzzy-neuro expert system for daily temperature forecasting, Knowledge-Based Systems, 46 (2013), 12-21.

[42] P. Singh, B. Borah, Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization, International Journal of Approximate Reasoning, 55 (2014), 812-833.

[43] Q. Song, B. S. Chissom, Fuzzy time series, its models, Fuzzy Sets and Systems, 54 (1993), 269-277.

[44] M. Sugeno, An introductory survey of fuzzy control, Information Sciences, 36 (1985), 59-83.

[45] F. M. Talarposhtia, J. S. Hossein, E. Rasul, F. G. Guimaraesc, M. Mahmud, T. Eslami, Stock market forecasting by using a hybrid model of exponential fuzzy time series, International Journal of Approximate Reasoning, 70 (2016), 79-98.

[46] H. Tong, Nonlinear time series: A dynamical system approach, Oxford University Press, Oxford, 1990.

[47] S. Torbat, M. Khashei, M. Bijari, A hybrid probabilistic fuzzy ARIMA model for consumption forecasting in commodity markets, Economic Analysis And Policy, 58 (2018), 22-31.

[48] F. M. Tseng, G. H. Tzeng, A fuzzy seasonal ARIMA model for forecasting, Fuzzy Sets and Systems, 126 (2002), 367-376.

[49] V. R. Uslu, E. Bas, U. Yolcu, E. Egrioglu, A fuzzy time series approach based on weights determined by the number of recurrences of fuzzy relations, Swarm and Evolutionary Computation, 15 (2014), 19-26.

[50] W. Wang, X. Liu, Fuzzy forecasting based on automatic clustering and axiomatic fuzzy set classification, Information Sciences, 294 (2015), 78-94.

[51] L. Y. Wei, A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting, Applied Soft Computing, 42 (2016), 368-376.

[52] W. A. Woodward, H. L. Gray, A. C. Elliott, Applied time series analysis, CRC Press, 2012.

[53] F. Ye, L. Zhang, D. Zhang, H. Fujita, Z. Gong, A novel forecasting method based on multiorder fuzzy time series and technical analysis, Information Sciences, 367-368 (2016), 41-57.

[54] O. C. Yolcu, F. Alpaslan, Prediction of TAIEX based on hybrid fuzzy time series model with single optimization process, Applied Soft Computing, 66 (2018), 18-33.

[55] O. C. Yolcu, H. K. Lam, A combined robust fuzzy time series method for prediction of time series, Neurocomput, 247 (2017), 87-101.

[56] O. C. Yolcu, U. Yolcu, E. Egrioglu, C. H. Aladag, High order fuzzy timeseries forecasting method based on an intersection operation, Applied Mathematical Modelling, 40 (2016), 8750-8765.

[57] H. K. Yu, Weighted fuzzy time-series models for TAIEX forecasting, Physica A: Statistical Mechanics and its Applications, 349(3) (2005), 609-624.

[58] R. Zarei, M. Gh. Akbari, J. Chachi, Modeling autoregressive fuzzy time series data based on semi-parametric methods, Soft Computing, 8 (2019), 121-128.