文件名称:lyaprosen
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INPUTES:
y: y is vector of values(time series data)
tau: embedding lag of state space reconstruction. When you have not
any information about tau please let it zero. The code will calculates
the tau.
m: m is embedding dimension. If you have not any information about
embedding dimension please let it zero. the code will find proper
embedding dimension.
OUTPUTS:
LLE: Largest Lyapunov Exponent
lambda: Lyapunov exponents for various ks. Plot of this exponents is
very helpful. If embedding dimension be selected correctly lambda curve
will have smooth part(or fairly horizontal). If there is no smooth
section on the curve, it is better you try with other embedding
dimensions.- INPUTES:
y: y is vector of values(time series data)
tau: embedding lag of state space reconstruction. When you have not
any information about tau please let it zero. The code will calculates
the tau.
m: m is embedding dimension. If you have not any information about
embedding dimension please let it zero. the code will find proper
embedding dimension.
OUTPUTS:
LLE: Largest Lyapunov Exponent
lambda: Lyapunov exponents for various ks. Plot of this exponents is
very helpful. If embedding dimension be selected correctly lambda curve
will have smooth part(or fairly horizontal). If there is no smooth
section on the curve, it is better you try with other embedding
dimensions.
y: y is vector of values(time series data)
tau: embedding lag of state space reconstruction. When you have not
any information about tau please let it zero. The code will calculates
the tau.
m: m is embedding dimension. If you have not any information about
embedding dimension please let it zero. the code will find proper
embedding dimension.
OUTPUTS:
LLE: Largest Lyapunov Exponent
lambda: Lyapunov exponents for various ks. Plot of this exponents is
very helpful. If embedding dimension be selected correctly lambda curve
will have smooth part(or fairly horizontal). If there is no smooth
section on the curve, it is better you try with other embedding
dimensions.- INPUTES:
y: y is vector of values(time series data)
tau: embedding lag of state space reconstruction. When you have not
any information about tau please let it zero. The code will calculates
the tau.
m: m is embedding dimension. If you have not any information about
embedding dimension please let it zero. the code will find proper
embedding dimension.
OUTPUTS:
LLE: Largest Lyapunov Exponent
lambda: Lyapunov exponents for various ks. Plot of this exponents is
very helpful. If embedding dimension be selected correctly lambda curve
will have smooth part(or fairly horizontal). If there is no smooth
section on the curve, it is better you try with other embedding
dimensions.
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lyaprosen.m