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.