English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
/persons/resource/persons173613

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

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
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.