Fuzzy Systems and its Applications

Fuzzy Systems and its Applications

Modeling of Fuzzy and Real Data based on the Support Vector Machine

Document Type : Original Article

Authors
mohammadghasem akbari 1
Hamid Hoseinkhani 2
1 department of statistic , birjand
2 Department of Statistics, University of Birjand, Birjand, Iran
10.22034/jfsa.2025.507136.1263
Abstract
In the time series analysis, we may encounter situations where some elements of the model are imprecise quantities. One of the most common situations is the inaccuracy of the underlying observations. In this paper, a new fuzzy time series model based on support vector machine approach is proposed. For this purpose, using support vector machine based Kernel function and loss function have been used to model fuzzy and real time series. In order to examine the performance and effectiveness of the proposed fuzzy time series model, some goodness of fit criteria is used. The results obtained based on one example of simulated fuzzy time series data and one real examples showed that the proposed method had a better performance compared to other existing methods.
Keywords
Oss Function
Support Vector Mashin
Kernel Function
Data time series
Subjects
[4]    Asadolahi, M., Akbari, M.G., Hesamian, G., and Arefi, M. (2021) A robust support vector regression with exact predictors and fuzzy responses. International Journal of Approximate Reasoning, 132, 206-225.
[5]    Bazaraa, M. S., Jarvis, J. J. and Sherali, H. D. (2010) Linear Programming and Network Flows, 4th Edition. New York: Wiley.
[6]    Cai, z. (2001) ”Weighted Nadaraya-Watson regression estimation”, Statistics and Probability Letters, 51,307-318.
[7]    Chen, S. (1996) Forecasting enrollments based on fuzzy time series, Fuzzy Sets and Systems, 81, 311–319.
[8]    Doroudyan, M. H. and Niaki, S. T. A. (2021) Pattern recognition in Financial surveillance with the ARMA-GARCH time series model using support vector machine , Expert Systems with Applications.
[9]    Ethem, A. (2004) Introduction to Machine Learning. The MIT Press Cambridge, Massachusetts Lon- don.
 
[10]    GascardJinlun, G.C. and Rafizadeh, Z. (2019) ”Rapid decline of arctic sea ice volume, causes and consequences”, The Cryosphere Discuss, 1-29.
[11]    Gui, B., Wei, X., Shen, Q., Qi, J. and Guo, L. (2015) Financial time series forecasting using support vector machine, IEEE.
[12]    Hesamian, G. and Akbari, M.G. (2018) A semi-parametric model for time series based on fuzzy data. Iranian Journal of Fuzzy Systems, 26, 2953-2666.
[13]    Hesamian, G., Torkian, F. and Yarmohammadi, M. (2022) A fuzzy non-parametric time series model based on fuzzy data. Iranian Journal of Fuzzy Systems, 19, 61-72.
[14]    Hojati, M., Bector, C.R. and Smimou, K. (2005) A simple method for computation of fuzzy linear regression. European Journal of Operational Research, 166, 172–184.
[15]    Hong, D. (2005) A note on fuzzy time-series model. Fuzzy Sets and Systems, 155,309–316.
[16]    Izenman, A. J. (2008) Modern Multivariate Statistical Techniques (Regression, Classification, and Manifold Learning), International Statistical Review.
[17]    Ruan, J., Wang, X. and Shi, Y. (2013) Developing fast predictors for large-scale time series using fuzzy granular support vector machines . Applied Soft Computing Journal, 13, 3981-4000.
[18]    Song, Q. and Chissom, B. S. (1993) Fuzzy time series and its models. Fuzzy Sets and Systems, 54, 269–277.
[19]    Song, Q., Leland, R. P. and Chissom, B. S. (1995) A new fuzzy time-series model of fuzzy number observations. fuzzy sets and systems, 73, 341–348.
[20]    Suykens, J. A. K., Gestel, J. V., Brabanter, J. D., Moor, B. D. and Vandewalle, J. (2002) Least squares support vector machines. World Scientific Pub. Co., Singapore, 29-55.
[21]    Vapnik, V. (1995) The Nature of Statistical Learning Theory, Springer-Verlag . New York.
[22]    Zadeh, L.A. (1956) ”Fuzzy sets”. Information and Control, 8, 338-353.
[23]    Zarei, R., Akbari, M. G. and Chaci, J. (2020) Modeling autoregressive fuzzy time series data based on semi-parametric methods. Soft Computing, 24, 7295–7304.
Fuzzy Systems and its Applications
Volume 8, Issue 1 - Serial Number 16
Open Access Statement
June 2025
Pages 145-165

  • Receive Date 17 February 2025
  • Revise Date 10 June 2025
  • Accept Date 15 July 2025