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Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis

Laurenz Wiskott
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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.

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BibTeXRIS

Laurenz Wiskott. 2003-12-12. Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis. https://arxiv.org/abs/cond-mat/0312317

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