Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis

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dc.creatorWiskott, Laurenz
dc.date2003-12-12
dc.date.accessioned2026-07-07T02:55:22Z
dc.date.available2026-07-07T02:55:22Z
dc.descriptionSlow 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.
dc.description8 pages, 4 figures
dc.identifierhttps://arxiv.org/abs/cond-mat/0312317
dc.identifierhttp://arxiv.org/abs/cond-mat/0312317
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/22917
dc.subjectStatistical Mechanics
dc.titleEstimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis
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