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Convolutional autoencoder and conditional random fields hybrid for predicting spatial-temporal chaos. (English) Zbl 1429.37047
Summary: 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.
©2019 American Institute of Physics

MSC:
37M10 Time series analysis of dynamical systems
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
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