e-Print archive, (2003-12-12) (bibtex, paper, paper.pdf,

Estimating driving forces of nonstationary time series with slow feature analysis.

Laurenz Wiskott

Abstract: 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.

Keywords: driving force, nonlinear time series analysis, nonstationary time series, slow feature analysis

Relevant Project:

November 16, 2006, Laurenz Wiskott,