Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis

Details Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis

2003-12-12

arxiv.org/abs/cond-mat/0312317v1

Physics

Condensed Matter

Statistical Mechanics

8 pages, 4 figures

Scientific paper

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.

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