Abstract
Extreme Learning Machines (ELMs) and Echo State Networks (ESNs) represent promising alternatives in time series forecasting in view of their intrinsic trade-off between performance and mathematical tractability. Both approaches share a key feature: their supervised parameter adaptation is restricted to the output layer, the remaining synaptic weights being chosen according to a priori unsupervised schemes. This work performs a comparative investigation regarding the performances of a classic ELM and ESNs in the context of the prediction of monthly seasonal streamflow series associated with Brazilian hydroelectric plants. With respect to the ESN, two possible reservoir design approaches are tested, as well as the novel architecture of Boccato et al., which is characterized by the use a Volterra filter and PCA in the readout. Additionally, a MLP is included to establish a base for comparison. Results show the relevance of these architectures in modeling seasonal streamflow series.
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
Boccato, L., Lopes, A., Attux, R., Von Zuben, F.J.: An Echo State Network Architecture Based on Volterra Filtering and PCA With Application to the Channel Equalization Problem. In: 2011 IJCNN, pp. 580–587. IEEE (2011)
Boccato, L., Lopes, A., Attux, R., Von Zuben, F.J.: An Extended Echo State Network Using Volterra Filtering and Principal Component Analysis. Neural Networks 32, 292–302 (2012)
Box, G., Jenkins, G.M., Reinsel, G.: Time Series Analysis: Forecasting & Control, 3rd edn. Prentice Hall, Holden Day, Oakland, California (1994)
Dos Santos, E.P., Von Zuben, F.J.: Improved Second-Order Training Algorithms for Globally and Partially Recurrent Neural Networks. In: Proceedings of the Int. Joint Conf. on Neural Networks, IJCNN 1999, pp. 1501–1506 (1999)
Haykin, S.: Adaptive Filter Theory. Prentice Hall (1997)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall (1999)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme Learning Machine: Theory and Applications. Neurocomputing 70, 489–501 (2006)
Huang, G.B., Chen, L., Siew, C.K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 17, 879–892 (2006)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, New York (2001)
Jaeger, H.: The Echo State Approach to Analyzing and Training Recurrent Neural Networks. German National Research Center for Information Technology. Tech. Rep. 148 (2001)
Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3, 127–149 (2009)
Luna, I., Ballini, R.: Top-Down Strategies Based on Adaptive Fuzzy Rule-Based Systems for Daily Time Series Forecasting. International Journal of Forecasting, 1–17 (2011)
Ozturk, M.C., Xu, D., Príncipe, J.C.: Analysis and Design of Echo State Networks. Neural Computation 19, 111–138 (2007)
Sacchi, R., Ozturk, M.C., Príncipe, J.C., Carneiro, A.A.F., da Silva, I.N.: Water Inflow Forecasting Using the Echo State Network: a Brazilian Case Study. In: Proceedings of Int. Joint Conf. on Neural Networks, IJCNN 2007, pp. 2403–2408. IEEE (2007)
Siqueira, H., Boccato, L., Attux, R., Filho, L.C.: Seasonal Streamflow Series Forecasting Using Echo State Networks. In: 10th Brazilian Congress on Computational Intelligence, Fortaleza-CE, Brazil (2011); (Previsão de séries de vazões com redes neurais de estados de eco) (in Portuguese)
ONS - Electric System National Operator - Brazil, http://www.ons.org.br/operacao/vazoes_naturais.aspx
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
Siqueira, H., Boccato, L., Attux, R., Lyra, C. (2012). Echo State Networks and Extreme Learning Machines: A Comparative Study on Seasonal Streamflow Series Prediction. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_60
Download citation
DOI: https://doi.org/10.1007/978-3-642-34481-7_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34480-0
Online ISBN: 978-3-642-34481-7
eBook Packages: Computer ScienceComputer Science (R0)