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Time-varying ARMA stable process estimation using sequential Monte Carlo

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Abstract

Various time series data in applications ranging from telecommunications to financial analysis and from geophysical signals to biological signals exhibit non-stationary and non-Gaussian characteristics. α-Stable distributions have been popular models for data with impulsive and non-symmetric characteristics. In this work, we present time-varying autoregressive moving-average α-stable processes as a potential model for a wide range of data, and we propose a method for tracking the time-varying parameters of the process with α-stable distribution. The technique is based on sequential Monte Carlo, which has assumed a wide popularity in various applications where the data or the system is non-stationary and non-Gaussian.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Renke Huang

  2. School of Electrical and Computer Engineering, Georgia Institute of Technology, 210 Technology Circle, Savannah, GA, 31407, USA

    Hao Zheng

  3. Images and Signals Laboratory, Institute of Science and Technology of Information, “A. Faedo” Italian National Council of Research (ISTI-CNR), via G. Moruzzi 1, Pisa, 56124, Italy

    Ercan E. Kuruoglu

Authors
  1. Renke Huang
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  2. Hao Zheng
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  3. Ercan E. Kuruoglu
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Corresponding authors

Correspondence to Hao Zheng or Ercan E. Kuruoglu.

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Huang, R., Zheng, H. & Kuruoglu, E.E. Time-varying ARMA stable process estimation using sequential Monte Carlo. SIViP 7, 951–958 (2013). https://doi.org/10.1007/s11760-011-0285-x

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  • DOI: https://doi.org/10.1007/s11760-011-0285-x

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