Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing

Abstract

The prediction of complex nonlinear dynamical systems with the help of machine learning techniques has become increasingly popular. In particular, reservoir computing turned out to be a very promising approach especially for the reproduction of the long-term properties of a nonlinear system. Yet, a thorough statistical analysis of the forecast results is missing. Using the Lorenz and Rössler system we statistically analyze the quality of prediction for different parametrizations - both the exact short-term prediction as well as the reproduction of the long-term properties (the "climate") of the system as estimated by the correlation dimension and largest Lyapunov exponent. We find that both short and longterm predictions vary significantly among the realizations. Thus special care must be taken in selecting the good predictions as predictions which deliver better short-term prediction also tend to better resemble the long-term climate of the system. Instead of only using purely random Erdös-Renyi networks we also investigate the benefit of alternative network topologies such as small world or scale-free networks and show which effect they have on the prediction quality. Our results suggest that the overall performance with respect to the reproduction of the climate of both the Lorenz and Rössler system is worst for scale-free networks. For the Lorenz system there seems to be a slight benefit of using small world networks while for the Rössler system small world and Erdös -Renyi networks performed equivalently well. In general the observation is that reservoir computing works for all network topologies investigated here.

arXiv comment10 pages, 5 figures, accepted for publication in Chaos

References in corpus (10)

• Statistical mechanics of complex networks
• Classes of behavior of small-world networks
• Scale-free networks are rare
• Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data
• Attractor Reconstruction by Machine Learning
• Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model
• Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronisation and cryptography
• Using reservoir computers to distinguish chaotic signals
• Network Structure Effects in Reservoir Computers
• Testing statistical laws in complex systems

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