Corpus ID: 12254643

Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis Laurenz Wiskott Institute for Theoretical Biology

@inproceedings{Wiskott2003EstimatingDF,
  title={Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis Laurenz Wiskott Institute for Theoretical Biology},
  author={Laurenz Wiskott},
  year={2003}
}
  • Laurenz Wiskott
  • Published 2003
  • Physics, Mathematics, Computer Science
  • 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 oset and a factor. Examples with a tent map and a logistic map illustrate the performance. 

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 33 CITATIONS

    Slow feature analysis

    VIEW 4 EXCERPTS

    Slowness as a Learning Principle

    VIEW 7 EXCERPTS
    CITES METHODS & BACKGROUND

    Slowness learning for curiosity-driven agents

    VIEW 4 EXCERPTS
    CITES BACKGROUND & METHODS
    HIGHLY INFLUENCED

    Monitoring of multivariate wind resources with self-organizing maps and slow feature analysis

    • Oliver Kramer, Tobias Hein
    • Engineering, Computer Science
    • 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG)
    • 2011
    VIEW 8 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    Extracting the driving force from ozone data using slow feature analysis

    VIEW 4 EXCERPTS
    CITES BACKGROUND & METHODS
    HIGHLY INFLUENCED

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 11 REFERENCES