Articles | Volume 22, issue 4
Research article
10 Jul 2015
Research article |  | 10 Jul 2015

Nonstationary time series prediction combined with slow feature analysis

G. Wang and X. Chen

Related authors

Cross-scale causal information flow from El Niño Southern Oscillation to precipitation in eastern China
Yasir Latif, Kaiyu Fan, Geli Wang, and Milan Palus
EGUsphere,,, 2024
Short summary
East Asia Reanalysis System (EARS)
Jinfang Yin, Xudong Liang, Yanxin Xie, Feng Li, Kaixi Hu, Lijuan Cao, Feng Chen, Haibo Zou, Feng Zhu, Xin Sun, Jianjun Xu, Geli Wang, Ying Zhao, and Juanjuan Liu
Earth Syst. Sci. Data, 15, 2329–2346,,, 2023
Short summary
On the interconnections among major climate modes and their common driving factors
Xinnong Pan, Geli Wang, Peicai Yang, Jun Wang, and Anastasios A. Tsonis
Earth Syst. Dynam., 11, 525–535,,, 2020
Short summary

Related subject area

Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
A comparison of two causal methods in the context of climate analyses
David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem
Nonlin. Processes Geophys., 31, 115–136,,, 2024
Short summary
A two-fold deep-learning strategy to correct and downscale winds over mountains
Louis Le Toumelin, Isabelle Gouttevin, Clovis Galiez, and Nora Helbig
Nonlin. Processes Geophys., 31, 75–97,,, 2024
Short summary
Downscaling of surface wind forecasts using convolutional neural networks
Florian Dupuy, Pierre Durand, and Thierry Hedde
Nonlin. Processes Geophys., 30, 553–570,,, 2023
Short summary
Superstatistical analysis of sea surface currents in the Gulf of Trieste, measured by high-frequency radar, and its relation to wind regimes using the maximum-entropy principle
Sofia Flora, Laura Ursella, and Achim Wirth
Nonlin. Processes Geophys., 30, 515–525,,, 2023
Short summary
The Sampling Method for Optimal Precursors of ENSO Event
Bin Shi and Junjie Ma
EGUsphere,,, 2023
Short summary

Cited articles

Berkes, P. and Wiskott, L.: Slow feature analysis yields a rich repertoire of complex cell properties, J. Vision, 5, 579–602, 2005.
Boucharel, J., Dewitte, B., Garel, B., and du Penhoat, Y.: ENSO's non-stationary and non-Gaussian character: the role of climate shifts, Nonlin. Processes Geophys., 16, 453–473,, 2009.
Brasseur, G. and Granier, C.: Mount Pinatubo aerosols, chlorofluorocarbons and ozone depletion, Science, 257, 1239–1242, 1992.
Casdagli, M.: Nonlinear prediction of chaotic time series, Phys. D., 35, 335–356, 1989.
Farmer, J. D. and Sidorowich, J.: Predicting chaotic time series, Phys. Rev. E., 59, 845–848, 1989.
Short summary
This paper presents a new technique of combining the driving force of a time series obtained using the slow feature analysis (SFA) approach, then introducing the driving force into a predictive model to predict nonstationary time series. It could be considered to be a data-driven attempt to make progress in predicting nonstationary climatic time series and in better understanding the climate causality research from observed climate data.