Constrained randomization of time series for hypothesis testing

We propose a general scheme to create time sequences that fulfill given constraints but are random otherwise. Significance levels for nonlinearity tests are as usually obtained by Monte Carlo resampling. In a new scheme, constraints including multivariate, nonlinear, and nonstationary properties are implemented in the form of a cost function.