Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis
arXiv:cond-mat/0312317
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.
8 pages, 4 figures