  Slow feature analysis (SFA) is a new technique for extracting slowly varying
features from a quickly varying signal. It is shown here that SFA can be
applied to nonstationary time series to estimate a single underlying driving
force with high accuracy up to a constant offset and a factor. Examples with a
tent map and a logistic map illustrate the performance.
