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Methods and Algorithms for Robust Filtering

In: COMPSTAT 2004 — Proceedings in Computational Statistics

Author

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  • Ursula Gather

    (University of Dortmund, Department of Statistics)

  • Roland Fried

    (University of Dortmund, Department of Statistics)

Abstract

We discuss filtering procedures for robust extraction of a signal from noisy time series. Moving averages and running medians are standard methods for this, but they have shortcomings when large spikes (outliers) respectively trends occur. Modified trimmed means and linear median hybrid filters combine advantages of both approaches, but they do not completely overcome the difficulties. Improvements can be achieved by using robust regression methods, which work even in real time because of increased computational power and faster algorithms. Extending recent work we present filters for robust online signal extraction and discuss their merits for preserving trends, abrupt shifts and extremes and for the removal of spikes.

Suggested Citation

  • Ursula Gather & Roland Fried, 2004. "Methods and Algorithms for Robust Filtering," Springer Books, in: Jaromir Antoch (ed.), COMPSTAT 2004 — Proceedings in Computational Statistics, pages 159-170, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2656-2_12
    DOI: 10.1007/978-3-7908-2656-2_12
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