Time-varying ARMA stable process estimation using sequential Monte Carlo

被引：0
作者：
Renke Huang
Hao Zheng
Ercan E. Kuruoglu
机构：
[1] Georgia Institute of Technology,School of Electrical and Computer Engineering
[2] Georgia Institute of Technology,School of Electrical and Computer Engineering
[3] Institute of Science and Technology of Information,Images and Signals Laboratory
[4] “A. Faedo” Italian National Council of Research (ISTI-CNR),undefined
来源：
Signal, Image and Video Processing | 2013年 / 7卷
关键词：
-Stable processes; Time-varying processes; Sequential Monte Carlo;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：951 / 958
页数：7
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