Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis
نویسنده
چکیده
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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Nonstationary time series prediction combined with slow feature analysis
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