Abstract
In recent years, there are several ongoing efforts to develop models for forecasting fuzzy time series using classical or artificial intelligence (AI) techniques in different application areas. A major challenge lying with the fuzzy time series forecasting model is efficient partitioning of data. It has significant effect on forecasting accuracy. Proposed work overcomes the difficulty of searching appropriate interval length for partitioning the data. In this study, a hybrid model using particle swarm optimization and backpropagation neural network (BPNN) is applied for forecasting fuzzy time series. Particle swarm optimization (PSO) searches for optimal partitioning of data, and weights of neural network are adjusted using gradient descent technique. The neural network takes fuzzy membership values as input, and every particle represents a set of boundaries between two adjacent intervals. This hybrid procedure is iterated until stopping condition is reached. The experiment is carried out on standard datasets, and results are compared with related models including neuro-fuzzy models applied on the same dataset. Proposed idea shows best performance in terms of accuracy in prediction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aladag, C.H., Basaran, M.A., Egrioglu, E.: A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks. Math. Comput. Simul. 81(4), 875–882 (2010)
Aladag, C.H.: Using multiplicative neuron model to establish fuzzy logic relationships. Expert Syst. Appl. 40(3), 850–853 (2013)
Aliev, R., Fazlollahi, B., Aliev, R., Guirimov, B.: Fuzzy time series prediction method based on fuzzy recurrent neural network. In: King, I., et al. (eds.) ICONIP, Part II. LNCS, vol. 4233, pp. 860–869. Springer, Berlin (2006)
Alpaydın, E.: Introduction to Machine Learning, 2nd edn. The MIT Press, Cambridge, Massachusetts (2010)
Basser, H., Karami, H., Shamshirband, S., Akib, S., Amirmojahedi, M., Ahmad, R., Jahangirzadeh, A., Javidnia, H.: Hybrid ANFIS-PSO approach for predicting optimum parameters of a protective spur dike. Appl. Soft Comput. 30, 642–649 (2015)
Bas, E., Egrioglu, E., Aladag, C.H., Yolcu, U.: Fuzzy-time-series network used to forecast linear and nonlinear time series. Appl. Intell. 43, 343–355 (2015)
Bas, E., Uslu, V.R., Yolcu, U., Egrioglu, E.: A modified genetic algorithm for forecasting fuzzy time series. Appl. Intell. 41(2), 453–463 (2014)
Bose, M., Mali, K.: High order time series forecasting using fuzzy discretization. Int. J. Fuzzy Syst. Appl. 5(4) (2016). https://doi.org/10.4018/ijfsa.2016100107
Cai, Q., Zhang, D., Zheng, W., Leung, S.C.H.: A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression. Knowl.-Based Syst. 74, 61–68 (2015)
Chen, S.M.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81, 311–319 (1996)
Chen, M.Y.: A high-order fuzzy time series forecasting model for inter net stock trading. Future Gener. Comput. Syst. 37, 461–467 (2014)
Chen, S.M., Kao, P.Y.: TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines. Inf. Sci. 247, 62–71 (2013)
Cheng, C.H., Chen, T.L., Wei, L.Y.: A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Inf. Sci. 180(9), 1610–1629 (2010)
Cheng, S.-H., Chen, S.-M., Jian, W.-S.: Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Inf. Sci. 327, 272–287 (2016)
Egrioglu, E., Aladag, C.H., Yolcu, U.: Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Syst. Appl. 40, 854–857 (2013)
Huarng, K., Yu, H.-K.: The application of neural networks to forecast fuzzy time series. Phys. A 363(2), 481–491 (2006)
Hsu, L.-Y., Horng, S.-J., Kao, T.-W., Chen, Y.-H., Run, R.-S., Chen, R.-J., Lai, J.-L., Kuo, I.-H.: Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques. Expert Syst. Appl. 37, 2756–2770 (2010)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, IV, Piscataway, NJ, pp. 1942–1948 (1995)
Lee, L.W., Wang, L.H., Chen, S.M., Leu, Y.H.: Handling fore casting problems based on two-factors high-order fuzzy time series. IEEE Trans. Fuzzy Syst. 14(3), 468–477 (2006)
Lu, W., Chen, X., Pedrycz, W., Liu, X., Yang, J.: Using interval information granules to improve forecasting in fuzzy time series. Int. J. Approximate Reasoning 57, 1–18 (2015)
Oliveira, M.V., Schirru, R.: Applying particle swarm optimization algorithm for tuning a neuro-fuzzy inference system for sensor monitoring. Prog. Nucl. Energy 51, 177–183 (2009)
Qasem, S.N, Shamsuddin, S.M.: Hybrid learning enhancement of RBF network based on particle swarm optimization. In: Yu, W., He, H., Zhang, N. (eds.) Part III. LNCS, vol. 5553, pp. 19–29 Springer, Berlin (2009)
Rumelhart, D.E., Mcclelland, J.L. (eds.): Parallel Distributed Prosessing, vol. 1. MIT press, Cambridge, MA (1986)
Singh, P., Borah, B.: Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization. Int. J. Approximate Reasoning 55, 812–833 (2014)
Singh, P.: High-order fuzzy-neuro-entropy integration-based expert system for time series forecasting. Neural Comput. Appl. (2016a). https://doi.org/10.1007/s00521-016-2261-4
Singh, P., Borah, B.: High-order fuzzy-neuro expert system for daily temperature Forecasting. Knowl.-Based Syst. 46, 12–21 (2013)
Singh, P.: Rainfall and financial forecasting using fuzzy time series and neural networks based model. Int. J. Mach. Learn. Cybern. (2016b). https://doi.org/10.1007/s13042-016-0548-5
Song, Q., Chissom, B.S.: Fuzzy time series and its models. Fuzzy Sets Syst. 54, 269–277 (1993)
Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series—part I. Fuzzy Sets Syst. 54, 1–9 (1993)
Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series—part II. Fuzzy Sets Syst. 64, 1–8 (1994)
Sun, B., Guo, H., Karimi, H.R., Ge, Y., Xiong, S.: Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series. Neurocomputing 151, 1528–1536 (2015)
Yu, H.-K., Huarng, K.: A bivariate fuzzy time series model to forecast TAIEX. Expert Syst. Appl. 34, 2945–2952 (2008)
Yu, T.H.-K., Huarng, K.: A neural network-based fuzzy time series model to improve forecasting. Expert Syst. Appl. 37, 3366–3372 (2010)
Yu, H.-K.: Weighted fuzzy time series models for TAIEX fore casting. Phys. A 349(3–4), 609–624 (2005)
Zadeh, L.A.: Fuzzy set. Inf. Control 8, 338–353 (1965)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—part I. Inf. Sci. 8, 199–249 (1975)
Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
Zhang, J.-R., Zhang, J., Lok, T.-M., Lyu, M.R.: A hybrid particle swarm optimization–back-propagation algorithm for feed forward neural network training. Appl. Math. Comput. 185, 1026–1037 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bose, M., Mali, K. (2019). Fuzzy Time Series Forecasting Model Using Particle Swarm Optimization and Neural Network. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_32
Download citation
DOI: https://doi.org/10.1007/978-981-13-1592-3_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1591-6
Online ISBN: 978-981-13-1592-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)