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Related Experiment Videos

A new algorithm for autoregression moving average model parameter estimation using group method of data handling.

K H Chon1, S Lu

  • 1Department of Electrical Engineering and Center for Biomedical Engineering, City College of the City University of New York, NY 10031, USA. kichon@ee-mail.engr.ccny.cuny.edu

Annals of Biomedical Engineering
|February 24, 2001
PubMed
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A novel Group Method of Data Handling (GMDH) algorithm enhances autoregressive moving average (ARMA) parameter estimation. This self-organizing method shows improved performance over traditional algorithms in noisy conditions and with incorrect model orders.

Area of Science:

  • Signal Processing
  • Machine Learning
  • Time Series Analysis

Background:

  • Autoregressive Moving Average (ARMA) models are fundamental in time series analysis.
  • Accurate parameter estimation is crucial for effective ARMA modeling.
  • Existing methods like Fast Orthogonal Search (FOS) and least-squares have limitations.

Purpose of the Study:

  • Introduce a modified Group Method of Data Handling (GMDH) algorithm for ARMA parameter estimation.
  • Evaluate the performance of the GMDH algorithm for ARMA models.
  • Compare GMDH with established FOS and least-squares methods.

Main Methods:

  • The study adapted the heuristic, self-organizing GMDH algorithm for ARMA parameter estimation.
  • Computer simulations were conducted to assess the algorithm's efficacy.

Related Experiment Videos

  • Performance was benchmarked against Fast Orthogonal Search (FOS) and least-squares methods.

    • Main Results:

    • The modified GMDH algorithm demonstrated robust performance in parameter estimation for ARMA models.
    • GMDH outperformed FOS and least-squares methods in scenarios with noise contamination.
    • The algorithm effectively identified true model parameters even with incorrect model order assumptions.

    Conclusions:

    • The GMDH-based approach offers a promising alternative for ARMA parameter estimation.
    • This method shows particular advantages in complex and noisy time series data.
    • The self-organizing nature of GMDH allows for adaptive model complexity determination.