A Fuzzy Time Series Model Based on Improved Fuzzy Function and Cluster Analysis Problem

Abstract

Based on the improvement in establishing the relations of data, this study proposes a new fuzzy time series model. In this model, the suitable number of fuzzy sets and their specific elements are determined automatically. In addition, using the percentage variations of series between consecutive periods of time, we build the fuzzy function. Incorporating all these improvements, we have a new fuzzy time series model that is better than many existing ones through the well-known data sets. The calculation of the proposed model can be performed conveniently and efficiently by a MATLAB procedure . The proposed model is also used in forecasting for an urgent problem in Vietnam. This application also shows the advantages of the proposed model and illustrates its effectiveness in practical application.

Keywords

Cluster analysis / Forecast / Fuzzy time series / Model

References

[1] Abbasov, A. M., Mamedova, M. H.: Application of fuzzy time series to population forecasting. In: CORP, Vienna University of Technology, pp. 545–552(2003)

[2] Aladag CH, Basaran MA, Egrioglu E, Yolcu U, Uslu VR. Forecasting in high order fuzzy time series by using neural networks to define fuzzy relations. Expert Syst. Appl.. 2013, 36 4228-4231

[3] Aladag CH, Yolcu U, Egrioglu E, Dalar AZ. A new time invariant fuzzy time series forecasting method based on particle swarm optimization. Appl. Soft Comput.. 2012, 12 3291-3299

[4] Chen SM. Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst.. 1996, 81 311-319

[5] Chen SM, Hsu C. A new method to forecast enrollments using fuzzy time series. Int. J. Appl. Sci. Eng.. 2004, 2 3234-3244

[6] Chen SM, Kao PY. TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines. Inf. Sci.. 2013, 247 62-71

[7] Chen JH, Hung WL. An automatic clustering algorithm for probability density functions. J. Stat. Comput. Simul.. 2015, 85 3047-3063

[8] Cheng C, Chen T, Teoh C. Fuzzy time-series based on adaptive expectation model for Taifex forecasting. Expert Syst. Appl.. 2008, 34 1126-1132

[9] Dincer NG, Akkus O. A new fuzzy time series model based on robust clustering for forecasting of air pollution. Ecol. Inf.. 2017

[10] Egrioglu E, Aladag C, Yolcu U, Uslu U, Basaran MA. A new approach based on artificial neural networks for high order multivariate fuzzy time series. Expert Syst. Appl.. 2009, 36 10589-10594

[11] Egrioglu E, Uslu V, Yolcu U, Basaran M, Aladag C. A new approach based on artificial neural networks for high order bivariate fuzzy time series. Appl. Soft Comput.. 2009, 58 265-273

[12] Egrioglu S, Bas E, Aladag CH, Yolcu U. Probabilistic fuzzy time series method based on artificial neural network. Am. J. Intell. Syst.. 2016, 62 42-47

[13] Eren B, Vedide R, Uslu U, Erol E. A modified genetic algorithm for forecasting fuzzy time series. Appl. Intell.. 2014, 41 453-463

[14] Ghosh H, Chowdhury S. An improved fuzzy time-series method of forecasting based on L-R fuzzy sets and its application. J. Appl. Stat.. 2016, 43 1128-1139

[15] Hao T. An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers. Fuzzy Optim. Decis. Mak.. 2016, 6 63-80

[16] Huarng K. Heuristic models of fuzzy time-series for forecasting. Fuzzy Sets Syst.. 2001, 123 369-386

[17] Huarng K. Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst.. 2001, 12 387-394

[18] Huarng K, Yu T. Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Trans. Syst. Man Cybern. Part B Cybern.. 2001, 36 328-340

[19] Khashei M, Bijari M, Hejazi CSR. An extended fuzzy artificial neural networks model for time series forecasting. Iran. J. Fuzzy Syst.. 2011, 3 45-66

[20] Lee H, Chou M. Fuzzy forecasting based on fuzzy time series. Int. J. Comput. Math.. 2004, 81 781-789

[21] Qiang S, Brad C. Forecasting enrollments with fuzzy time series—Part II. Fuzzy Sets Syst.. 1994, 62 1-8

[22] Richard JH, James CB. Recent convergence results for the fuzzy c-means clustering algorithms. J. Classif.. 1998, 5 237-247

[23] Singh S. A simple method of forecasting based on fuzzy time-series. Appl. Math. Comput.. 1998, 186 330-339

[24] Singh S. A computational method of forecasting based on high-order fuzzy time series. Expert Syst. Appl.. 2009, 36 10551-10559

[25] Song Q, Chissom B. Fuzzy time series and its models. Fuzzy Sets Syst.. 1993, 54 269-277

[26] Song Q, Chissom B. Forecasting enrollments with fuzzy time series—part I. Fuzzy Sets Syst.. 1993, 54 1-9

[27] Song Q, Chissom B. Forecasting enrollments with fuzzy time series—part II. Fuzzy Sets Syst.. 1994, 62 1-8

[28] Sullivan J, Woodall W. A comparison of fuzzy forecasting and markov modeling. Fuzzy Sets Syst.. 1994, 64 279-293

[29] Teoh H, Cheng C, Chu H, Chen J. Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets. Data Knowl. Eng.. 2008, 67 103-117

[30] Tai VV. An improved fuzzy time series forecasting model using variations of data. Fuzzy Optim. Decis. Mak.. 2019, 18 151-173

[31] Tai VV, Nghiep LD. A New fuzzy time series model based on cluster analysis problem. Int. J. Fuzzy Syst.. 2019, 21 852-864

[32] Zhiqiang Z, Qiong Z. Fuzzy time series forecasting based on k-means clustering. Open J. Appl. Sci.. 2012, 25 100-103

[33] Yu HK. Weighted fuzzy time-series models for TAIEX forecasting. Phys. A. 2005, 349 609-624

[34] Yu HK, Huarng K. A neural network- based fuzzy time series model to improve forecasting. Expert Syst. Appl.. 2010, 37 3366-3372