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Effective Signal Extraction Via Local Polynomial Approximation Under Long-Range Dependency Conditions

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Abstract

We study the signal extraction problemwhere a smooth signal is to be estimated against a long-range dependent noise. We consider an approach employing local estimates and derive a theoretically optimal (maximum likelihood) filter for a polynomial signal. On its basis, we propose a practical signal extraction algorithm and adapt it to the extraction of quasi-seasonal signals. We further study the performance of the proposed signal extraction scheme in comparison with conventional methods using the numerical analysis and real-world datasets.

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Authors and Affiliations

  1. Lomonosov Moscow State University, Complex Systems Modeling Laboratory, Lomonosovskii pr. 27-1, Moscow, 119991, Russia

    A. V. Artemov

  2. Yandex Data Factory, ul. L’va Tolstogo 16, Moscow, 119021, Russia

    A. V. Artemov

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  1. A. V. Artemov
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Correspondence to A. V. Artemov.

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(Submitted by E. K. Lipachev)

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Artemov, A.V. Effective Signal Extraction Via Local Polynomial Approximation Under Long-Range Dependency Conditions. Lobachevskii J Math 39, 309–320 (2018). https://doi.org/10.1134/S1995080218030101

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  • DOI: https://doi.org/10.1134/S1995080218030101

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