A new robust estimation method for ARMA models

  • Authors:

  • Yacine Chakhchoukh;Patrick Panciatici;Pascal Bondon;Lamine Mili

  • Affiliations:

  • RTE, DMA, Versailles, France;RTE, DMA, Versailles, France;CNRS, Univ. Paris-Sud - Gif-sur-Yvette, France;Virginia Tech-ECE Dept, Falls Church, USA

  • Venue:
  • ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year:
  • 2009

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Abstract

This paper presents a new robust method to estimate the parameters of ARMA models. This method makes use of the autocorrelations estimates based on the ratio of medians together with a robust filter cleaner able to reject a large fraction of outliers, and a Gaussian maximum likelihood estimation which handles missing values. The main advantages of the procedure are its easiness, robustness and fast execution. Its effectiveness is demonstrated on an example of the forecasting of the French daily electricity consumptions.