Forecasting Sequential Data Using Consistent Koopman Autoencoders

Jul 12, 2020

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Neural networks are widely used for processing time series data, yet such models often ignore the underlying physical structures in the input measurements. Recently Koopman-based models have been suggested, as a promising alternative to recurrent neural networks, for forecasting complex high-dimensional dynamical systems. We propose a novel Consistent Koopman Autoencoder that exploits the forward and backward dynamics to achieve long time predictions. Key to our approach is a new analysis where we unravel the interplay between invertible dynamics and their associated Koopman operators. Our architecture and loss function are interpretable from a physical viewpoint, and the computational requirements are comparable to other baselines. We evaluate the proposed algorithm on a wide range of high-dimensional problems, from simple canonical systems such as linear and nonlinear oscillators, to complex ocean dynamics and fluid flows on a curved domain. Overall, our results show that our model yields accurate estimates for significant prediction horizons, while being robust to noise in the input data.

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The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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