Convolutional autoencoder and conditional random fields hybrid for predicting 
spatial-temporal chaos

Herzog, S.; Wörgötter, F.; Parlitz, Ulrich

doi:10.1063/1.5124926

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Convolutional autoencoder and conditional random fields hybrid for predicting spatial-temporal chaos

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Parlitz, Ulrich
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Citation

Herzog, S., Wörgötter, F., & Parlitz, U. (2019). Convolutional autoencoder and conditional random fields hybrid for predicting spatial-temporal chaos. Chaos, 29(12): 123116. doi:10.1063/1.5124926.

Cite as: https://hdl.handle.net/21.11116/0000-0005-8106-2

Abstract

We present an approach for data-driven prediction of high-dimensional chaotic time series generated by spatially-extended systems. The algorithm employs a convolutional autoencoder for dimension reduction and feature extraction combined with a probabilistic prediction scheme operating in the feature space, which consists of a conditional random field. The future evolution of the spatially-extended system is predicted using a feedback loop and iterated predictions. The excellent performance of this method is illustrated and evaluated using Lorenz-96 systems and Kuramoto-Sivashinsky equations of different size generating time series of different dimensionality and complexity.