Articles | Volume 9, issue 3
Research article
 | Highlight paper
23 Jul 2018
Research article | Highlight paper |  | 23 Jul 2018

Using network theory and machine learning to predict El Niño

Peter D. Nooteboom, Qing Yi Feng, Cristóbal López, Emilio Hernández-García, and Henk A. Dijkstra

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Cited articles

Akaike, H.: A New Look at the Statistical Model Identification, IEEE T. Automat. Contr., AC-19, 716–723,, 1974.
Aladag, C. H., Egrioglu, E., and Kadilar, C.: Forecasting nonlinear time series with a hybrid methodology, Appl. Math. Lett., 22, 1467–1470,, 2009.
Al-Smadi, A. and Al-Zaben, A.: ARMA Model Order Determination Using Edge Detection: A New Perspective, Circuits, Systems Signal Processing, 24, 723–732, 2005.
Berezin, Y., Gozolchiani, A., Guez, O., and Havlin, S.: Stability of Climate Networks with Time, Sci. Rep.-UK, 2, 1–8,, 2012.
Bergmeir, C. and Benítez, J. M.: On the use of cross-validation for time series predictor evaluation, Inf. Sci. (Ny)., 191, 192–213,, 2012.
Short summary
The prediction of the El Niño phenomenon, an increased sea surface temperature in the eastern Pacific, fascinates people for a long time. El Niño is associated with natural disasters, such as droughts and floods. Current methods can make a reliable prediction of this phenomenon up to 6 months ahead. However, this article presents a method which combines network theory and machine learning which predicts El Niño up to 1 year ahead.
Final-revised paper