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Hey, awesome to see this in Julia! I remember first learning about this methods in a 2018/2019 meeting where Ed Ott presented their reservoir computing work for chaotic timeseries prediction, and we presented the work based on TimeseriesPrediction.jl, (now published here https://link.springer.com/article/10.1007/s00332-019-09588-7 )
In chaotic timeseries prediction it is customary to express the time axis in units of the Lyapunov time, 1/λ with λ the Maximum Lyapunov exponent. For many famous systems (e.g. Lorenz63) you can find its value online, or use the lyapunov function from DynamicalSystems.jl, https://juliadynamics.github.io/DynamicalSystems.jl/dev/chaos/lyapunovs/
The text was updated successfully, but these errors were encountered:
Thanks! This package is largely based on Ott's group work actually, I really enjoyed all of their exploration in the Echo State Network field! Thanks for the tip, I'm doing some refactoring at the moment and the new version of the documentation will have the plots in units of Lyapunov time.
Hey, awesome to see this in Julia! I remember first learning about this methods in a 2018/2019 meeting where Ed Ott presented their reservoir computing work for chaotic timeseries prediction, and we presented the work based on TimeseriesPrediction.jl, (now published here https://link.springer.com/article/10.1007/s00332-019-09588-7 )
In chaotic timeseries prediction it is customary to express the time axis in units of the Lyapunov time,
1/λ
withλ
the Maximum Lyapunov exponent. For many famous systems (e.g. Lorenz63) you can find its value online, or use thelyapunov
function from DynamicalSystems.jl, https://juliadynamics.github.io/DynamicalSystems.jl/dev/chaos/lyapunovs/The text was updated successfully, but these errors were encountered: