Abstract
Lately, the research related to time series forecasting has been an area of considerable interest in different fields. It is very important to predict the behavior of the time series but it is not an easy task. Several models to aim this issue have been developed over the years, taking into account their peculiarities. Artificial Neural Networks (ANNs) are one of them. ANNs received much attention, and a great number of papers have reported successful experiments and practical tests. In this paper, a hybrid approach is proposed based on Harmony Search (HS) to select the number of hidden neurons and their weights for Extreme Learning Machine (ELM) algorithm, called HS-ELM. In addition, we provide experimental results from the application of our algorithm HS-ELM in real stream flow time series to show its effectiveness and usefulness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting and Control. Wiley, San Francisco (2008)
Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural Networks for Short-term Load Forecasting: A Review and Evaluation. PAS 16(1), 44–55 (2001)
Huang, G.B., Wang, D.H., Lan, Y.: Extreme Learning Machines: a Survey. IJMLC, 1–16 (2011)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back-Propagation Errors. Nature 323, 533–536 (1986)
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A New Heuristic Optimization Algorithm: Harmony Search. Simulation 76(2), 60–68 (2001)
Kim, J.H., Geem, Z.W., Kim, E.: Parameter Estimation of the Nonlinear Muskingum Model using Harmony Search. JAWRA 37(5), 1131–1138 (2001)
Geem, Z.W., Kim, J.H., Loganathan, G.V.: Harmony Search Optimization: Application to Pipe Network Design. IJMS 22(2), 125–133 (2002)
Lee, K., Geem, Z.H.: A New Structural Optimization Method Based on the Harmony Search Algorithm. Computers and Structures 82(9-10), 781–798 (2004)
Geem, Z.W., Tseng, C.-L., Park, Y.-J.: Harmony Search for Generalized Orienteering Problem: Best Touring in China. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 741–750. Springer, Heidelberg (2005)
Lee, K., Geem, Z.H.: A New Meta-heuristic Algorithm for Continuous Engineering Optimization: Harmony Search Theory and Practice. CMAME 194(36-38), 3902–3933 (2005)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme Learning Machine: Theory and Applications. Neurocomputing 70, 489–501 (2006)
Silva, D.N.G., Pacifico, L.D.S., Ludermir, T.B.: An Evolutionary Extreme Learning Machine based on Group Search Optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 574–580 (2011)
Centro de Pesquisas de Energia Elétrica. Potencialidades de Utilização de Fontes Alternativas para o Atendimento de Energia Elétrica no Interior do Estado de Amazonas. Tech. Report. CEPEL DP/DEA, RJ. 32695/04, 49 p. (2004) (in Portuguese)
Guilhon, L.G.F.: Modelo Heurístico de Previsão de Vazões Naturais Médias Semanais Aplicado à Usina de Foz do Areia. Master’s Thesis, UFRJ (2003) (in Portuguese)
ONS - Operador Nacional do Sistema Elétrico (in Portuguese), http://www.ons.org.br
Valença, I., Ludermir, T., Valença, M.: Hybrid Systems to Select Variables for Time Series Forecasting using MLP and Search Algorithms. SBRN, 247–252 (2010)
Valença, I., Lucas, T., Ludermir, T., Valença, M.: Selecting Variables with Search Algorithms and Neural Networks to Improve the Process of Time Series Forecasting. IJHIS 8(3), 129–141 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Valença, I., Valença, M. (2012). Optimizing the Extreme Learning Machine Using Harmony Search for Hydrologic Time Series Forecasting. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-32639-4_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32638-7
Online ISBN: 978-3-642-32639-4
eBook Packages: Computer ScienceComputer Science (R0)