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A robust orthogonal algorithm for system identification and time-series analysis.

M J Korenberg1

  • 1Department of Electrical Engineering, Queen's University, Kingston, Ontario, Canada.

Biological Cybernetics
|January 1, 1989
PubMed
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This study introduces novel methods for creating concise sinusoidal models of biological time-series data. These techniques efficiently identify significant frequencies, offering a more economical and higher-resolution alternative to traditional Fourier analysis.

Area of Science:

  • * Time-series analysis
  • * Systems biology
  • * Signal processing

Background:

  • * Biological time-series data often requires accurate modeling for understanding underlying processes.
  • * Conventional Fourier series analysis has limitations in handling complex biological data, including non-commensurate frequencies and data gaps.

Purpose of the Study:

  • * To develop and illustrate methods for obtaining parsimonious sinusoidal series models of biological time-series data.
  • * To identify nonlinear systems with unknown structures using these novel methods.
  • * To provide a more economical and higher-resolution representation compared to Fourier series.

Main Methods:

  • * Utilizing fast and robust orthogonal searches for identifying significant frequencies within time-series data.

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  • * Employing a rapid search strategy for significant terms to include in system or time-series models.
  • * Developing methods capable of handling unequally-spaced or missing data, and noise-corrupted time-series.

    • Main Results:

    • * The developed methods yield a more economical sinusoidal series representation than traditional Fourier series.
    • * The approach automatically determines model order by prioritizing the most significant frequencies.
    • * Achieved higher resolution and demonstrated robustness against noise and data irregularities.

    Conclusions:

    • * The proposed methods offer a superior alternative for modeling biological time-series and identifying nonlinear systems.
    • * These techniques provide a more flexible and accurate sinusoidal series representation by relaxing constraints on frequency commensurability.
    • * The methods are effective even with incomplete or noisy data, enhancing their applicability in biological research.