Practical method for determining the minimum embedding dimension of a scalar time series

A practical method is proposed to determine the minimum embedding dimension from a scalar time series. It has the following advantages: (1) does not contain any subjective parameters except for the time-delay for the embedding; (2) does not strongly depend on how many data points are available; (3) can clearly distinguish deterministic signals from stochastic signals; (4) works well for time series from high-dimensional attractors; (5) is computationally efficient. Several time series are tested to show the above advantages of the method.