  We introduce a statistical method to detect nonlinearity and nonstationarity
in time series, that works even for short sequences and in presence of noise.
The method has a discrimination power similar to that of the most advanced
estimators on the market, yet it depends only on one parameter, is easier to
implement and faster. Applications to real data sets reject the null hypothesis
of an underlying stationary linear stochastic process with a higher confidence
interval than the best known nonlinear discriminators up to date.
