Abstract
The studied problem is prediction of time series based on preceding values of several time series (a multi-dimensional time series). Besides prediction itself, the task is finding precursors, i.e. determination of a set of the most significant input features in coordinates ”initial time series – lag”. A four-stage prediction algorithm based on neural network committee has been suggested, implemented and studied. The algorithm has been successfully tested on one model problem and on one real world problem.
This is a preview of subscription content, log in via an institution to check access.
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
Dolenko, S.A., Orlov, Y.V., Persiantsev, I.G., Shugai, J.S.: Neural network algorithm for events forecasting and its application to space physics data. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 527–532. Springer, Heidelberg (2005)
Dolenko, S.A., Orlov, Y. V., Persiantsev, I.G., Shugai, J. S.: Neural Network Algorithms for Analyzing Multidimensional Time Series for Predicting Events and Their Application to Study of Sun-Earth Relations. Pattern Recognition and Image Analysis 17, 584–591 (2007)
Shugai, J.S., Guzhva, A.G., Dolenko, S.A., Persiantsev, I.G.: An Algorithm for Construction of a Hierarchical Neural Network Complex for Time Series Analysis and its Application for Studying Sun-Earth Relations. In: 8th International Conference, Pattern Recognition and Image Analysis: New Information Technologies (PRIA-8-2007), vol. 2, pp. 355–358, Yoshkar-Ola (2007)
Guzhva, A.G., Dolenko, S.A., Persiantsev, I.G.: Comparative Analysis of Methods for Determination of Significance of Input Variables in Neural Network Based Modeling: Method of Comparison and Its Application to Known Real World Problems. In: Neuroinformatics-2008. IX All-Russian Scientific-Technical Conference, part 2, pp. 216–225. MEPhI Publishers, Moscow (2008) (in Russian)
Wolpert, D.H.: Stacked Generalization. Neural Networks 5, 241–259 (1992)
Dolenko, S.A., Orlov, Y. V., Persiantsev, I.G., Shugai, J. S.: A Search for Correlations in Time Series by Using Neural Networks. Pattern Recognition and Image Analysis 13, 441–446 (2003)
Gleisner, H., Lundstedt, H., Wintoft, P.: Predicting geomagnetic storms from solar-wind data using time-delay neural networks. Annales Geophysicae 14, 679–686 (1996)
World Data Center for Geomagnetism, Kyoto – Real-time (Quicklook) Dst index, http://swdcwww.kugi.kyoto-u.ac.jp/dst_realtime/index.html
ACE (Advanced Commission Explorer) Browse Data Information, http://www.srl.caltech.edu/ACE/ASC/browse/view_browse_data.html
http://www-psych.stanford.edu/~andreas/Time-Series/SantaFe.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dolenko, S., Guzhva, A., Persiantsev, I., Shugai, J. (2009). Multi-stage Algorithm Based on Neural Network Committee for Prediction and Search for Precursors in Multi-dimensional Time Series. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-04277-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
Online ISBN: 978-3-642-04277-5
eBook Packages: Computer ScienceComputer Science (R0)