Abstract
Forecasting is an important data analysis technique that aims to study historical data in order to explore and predict its future values. In fact, to forecast, different methods have been tested and applied from regression to neural network models. In this research, we proposed Elman Recurrent Neural Network (ERNN) to forecast the Mackey-Glass time series elements. Experimental results show that our scheme outperforms other state-of-art studies.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ammar, B., Cherif, F., Alimi, A.M.: Existence and uniqueness of pseudo almost-periodic solutions of recurrent neural networks with time-varying coefficients and mixed delays. IEEE Trans. Neural Netw. Learn. Syst. 23(1), 109–118 (2012)
Cao, Q., Ewing, B.T., Thomson, M.A.: Forecasting wind speed with recurrent neural network. Eur. J. Oper. Res. 221, 148–154 (2012)
Danko, B., Tomislav, B., Dubravko, M., Josip, K., Branko, N.: A comparison of feed-forward and recurrent neural networks in time series forecasting. In: IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (2012)
Elman, J.L.: Finding structure in time. Cogn. Sci. 14, 179–211 (1990)
Hopfiled, J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. USA 79, 2554–2558 (1982)
Jordan, M.: Attractor dynamics and parallelism in connectionist sequential machine. In: Proceedings of the Eighth Conference of the Cognitive Science Society (1986)
Lapedes, A.S., Farber, R.: Nonlinear signal processing using neural networks: prediction and system modeling, Los Alamos National Laboratory, Los Alamos, New Mexico, Technical report (1987)
Lawrence, R.: Using neural networks to forecast stock market prices, Department of Computer Science, University of Manitoba, Winnipeg, Canada, Technical report, December 1997
Mortiz, M., Stefan, L., Stefan, V.: Sales forecasting with partial recurrent neural networks: Empirical Insights and Benchmarking results. In: 48th Hawaii International Conference on System Sciences (2015)
Ulbricht, C.: Multi-recurrent networks for traffic forecasting. In: Proceedings of the Twelfth National Conference on Artificial Intelligence (AAI-94), Seattle, USA, vol. 2, pp. 883–888 (1994)
Wallace, M.P.: Neural networks and their applications to finance. Bus. Intell. J. 1, 67–77 (2008)
Mustfaraj, G., Lowry, G., Chen, J.: Prediction of room temperature and relative humidity by autoregressive linear and non-linear neural network models for an open office. Energy Buildings 43(2011), 1452–1460 (2011)
Guo-Rui, J., Pu, H., Yong-Jie, Z.: Wind speed forecasting based on support vector machine with forecasting error estimation. In: Proceedings of the Sixth International Conference on Machine Learning and Cybernetics (2007)
Brooks, C.: Introductory Econometrics for Finance. Cambridge University Press, Cambridge (2002)
Florance, M.M., Sawicz, M.S.: Positioning sales forecasting for better results. J. Bus. Forecast. 12, 15–17 (1993)
Kostas, H., Parth, P., Shainee, K., Rick, S.B., Alberto, J.L.: Multi-step forecasting of wave power using a nonlinear recurrent neural network. In: IEEE PES General Meeting (2014)
Park, D.C.: A time series data prediction scheme using bilinear recurrent neural network. In: 2010 International Conference on Information Science and Applications, Seoul, pp. 1–7 (2010)
Wutsqa, D.U., Kusumawati, R., Subekti, R.: The application of elman recurrent neural network model for forecasting consumer price index of education, recreation and sports in yogyakarta. In: 10th International Conference on Natural Computation (2014)
Acknowledgments
The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Krichene, E., Masmoudi, Y., Alimi, A.M., Abraham, A., Chabchoub, H. (2017). Forecasting Using Elman Recurrent Neural Network. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-53480-0_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53479-4
Online ISBN: 978-3-319-53480-0
eBook Packages: EngineeringEngineering (R0)