Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis

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.

arXiv comment8 pages, 4 figures

Cited by in corpus (5)

• Incremental Slow Feature Analysis: Adaptive and Episodic Learning from High-Dimensional Input Streams
• On the numeric stability of the SFA implementation sfa-tk
• How slow is slow? SFA detects signals that are slower than the driving force
• Robustifying automatic speech recognition by extracting slowly varying features
• A biologically plausible neural network for Slow Feature Analysis