Abstract
This model is developed from the model of Abbasov and Mamedova (2003) in which the parameters are investigated by methods and algorithm to obtain the most suitable values for each data set. The experiments on Azerbaijan’s population, Vietnam’s population and Vietnam’s rice production demonstrate the feasibility and applicability of the proposed methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbasov, A.M., Mamedova, M.H.: Application of fuzzy time series to population forecasting. Vienna Univ. Technol. 12, 545–552 (2003)
Box, G.E.P., Jenkins, G.M.: Time series analysis: forecasting and control. Holden-Day Series in Time Series Analysis, Revised edn. Holden-Day, San Francisco (1976)
Chen, S.M.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81(3), 311–319 (1996)
Chen, S.M., Hsu, C.C.: A new method to forecast enrollments using fuzzy time series. Int. J. Appl. Sci. Eng. 2(3), 234–244 (2004)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Durbin, J.: The fitting of time-series models. Revue de l’Institut International de Statistique 28(3), 233–244 (1960)
Engle, R.F.: Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50, 987–1007 (1982). Journal of the Econometric Society
Friedman, J.H.: Multivariate adaptive regression splines. Ann. Stat. 19, 1–67 (1991)
Galton, F.: Co-relations and their measurement, chiefly from anthropometric data. Proc. Roy. Soc. Lond. 45(273–279), 135–145 (1888)
Ghazali, R., Hussain, A.J., Al-Jumeily, D., Lisboa, P.: Time series prediction using dynamic ridge polynomial neural networks. In: 2009 Second International Conference on Developments in eSystems Engineering (DESE), pp. 354–363. IEEE (2009)
Gupta, S., Wang, L.P.: Stock forecasting with feedforward neural networks and gradual data sub-sampling. Aust. J. Intell. Inf. Process. Syst. 11(4), 14–17 (2010)
Huarng, K.: Heuristic models of fuzzy time series for forecasting. Fuzzy Sets Syst. 123(3), 369–386 (2001)
Lewis, P.A.W., Stevens, J.G.: Nonlinear modeling of time series using multivariate adaptive regression splines (MARS). J. Am. Stat. Assoc. 86(416), 864–877 (1991)
de Oliveira, J.F.L., Ludermir, T.B.: A distributed PSO-ARIMA-SVR hybrid system for time series forecasting. In: 2014 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 3867–3872. IEEE (2014)
Park, D.C.: A time series data prediction scheme using bilinear recurrent neural network. In: 2010 International Conference on Information Science and Applications (ICISA), pp 1–7. IEEE (2010)
Pearson, K.: Mathematical contributions to the theory of evolution. III. Regression, heredity, and panmixia. Philos. Trans. Roy. Soc. Lond. Ser. A Contain. Pap. Math. Phys. Character 187, 253–318 (1896)
Ren, Y., Suganthan, P.N., Srikanth, N., Amaratunga, G.: Random vector functional link network for short-term electricity load demand forecasting. Inf. Sci. 367, 1078–1093 (2016)
Sasu, A.: An application of fuzzy time series to the Romanian population. Bulletin Transilv. Univ. Brasov 3, 52 (2010)
Singh, S.R.: A computational method of forecasting based on fuzzy time series. Math. Comput. Simul. 79(3), 539–554 (2008). https://doi.org/10.1016/j.matcom.2008.02.026
Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series part I. Fuzzy Sets Syst. 54(1), 1–9 (1993)
Teo, K., Wang, L., Lin, Z.: Wavelet packet multi-layer perceptron for chaotic time series prediction: effects of weight initialization. In: Computational Science-ICCS 2001, pp 310–317 (2001)
Tseng, F.M., Tzeng, G.H.: A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets Syst. 126(3), 367–376 (2002). https://doi.org/10.1016/S0165-0114(01)00047-1. http://www.sciencedirect.com/science/article/pii/S0165011401000471
Wang, L., Fu, X.: Data Mining with Computational Intelligence. Springer Science & Business Media, New York (2006)
Wang, L., Teo, K.K., Lin, Z.: Predicting time series with wavelet packet neural networks. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2001. vol 3, pp 1593–1597. IEEE (2001)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Zecchin, C., Facchinetti, A., Sparacino, G., De Nicolao, G., Cobelli, C.: A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp 5653–5656. IEEE (2011)
Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
Zhu, M., Wang, L.: Intelligent trading using support vector regression and multilayer perceptrons optimized with genetic algorithms. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–5. IEEE (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Che-Ngoc, H., Vo-Van, T., Huynh-Le, QC., Ho, V., Nguyen-Trang, T., Chu-Thi, MT. (2018). An Improved Fuzzy Time Series Forecasting Model. In: Anh, L., Dong, L., Kreinovich, V., Thach, N. (eds) Econometrics for Financial Applications. ECONVN 2018. Studies in Computational Intelligence, vol 760. Springer, Cham. https://doi.org/10.1007/978-3-319-73150-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-73150-6_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73149-0
Online ISBN: 978-3-319-73150-6
eBook Packages: EngineeringEngineering (R0)