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    Near-ideal causal smoothing filters for the real sequences

    227789_161297_flterDTmy.pdf (143.4Kb)
    Access Status
    Open access
    Authors
    Dokuchaev, Nikolai
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Dokuchaev, N. 2015. Near-ideal causal smoothing filters for the real sequences. Signal Processing. 118: pp. 285-293.
    Source Title
    Signal Processing
    DOI
    10.1016/j.sigpro.2015.07.002
    ISSN
    0165-1684
    School
    Department of Mathematics and Statistics
    URI
    http://hdl.handle.net/20.500.11937/46183
    Collection
    • Curtin Research Publications
    Abstract

    The paper considers causal smoothing of the real sequences, i.e., discrete time processes in a deterministic setting. A family of causal linear time-invariant filters is suggested. These filters approximate the gain decay for some non-causal ideal smoothing filters with transfer functions vanishing at a point of the unit circle and such that they transfer processes into predictable ones. In this sense, the suggested filters are near-ideal; a faster gain decay would lead to the loss of causality. Applications to predicting algorithms are discussed and illustrated by experiments with forecasting of autoregressions with the coefficients that are deemed to be untraceable.

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