On the generation of correlated time series with a given probability density function

Sergey Primak; Vladimir Lyandres; Oren Kaufman; Mark Kliger

doi:10.1016/S0165-1684(98)00165-0

On the generation of correlated time series with a given probability density function

Sergey Primak, Vladimir Lyandres, Oren Kaufman, Mark Kliger

Department of Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

30 Scopus citations

Abstract

The approach presented provides very good results in modelling non-Gaussian time series. It is validated by direct numerical simulation of bimodal and Nakagami distributed sequences with approximately exponential correlation function.

Original language	English
Pages (from-to)	61-68
Number of pages	8
Journal	Signal Processing
Volume	72
Issue number	2
DOIs	https://doi.org/10.1016/S0165-1684(98)00165-0
State	Published - 15 Jan 1999

Keywords

Local linearization
Non-linear autoregression
PDF
Stochastic differential equation

ASJC Scopus subject areas

Control and Systems Engineering
Software
Signal Processing
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

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10.1016/S0165-1684(98)00165-0

Cite this

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title = "On the generation of correlated time series with a given probability density function",

abstract = "The approach presented provides very good results in modelling non-Gaussian time series. It is validated by direct numerical simulation of bimodal and Nakagami distributed sequences with approximately exponential correlation function.",

keywords = "Local linearization, Non-linear autoregression, PDF, Stochastic differential equation",

author = "Sergey Primak and Vladimir Lyandres and Oren Kaufman and Mark Kliger",

year = "1999",

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doi = "10.1016/S0165-1684(98)00165-0",

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T1 - On the generation of correlated time series with a given probability density function

AU - Primak, Sergey

AU - Lyandres, Vladimir

AU - Kaufman, Oren

AU - Kliger, Mark

PY - 1999/1/15

Y1 - 1999/1/15

N2 - The approach presented provides very good results in modelling non-Gaussian time series. It is validated by direct numerical simulation of bimodal and Nakagami distributed sequences with approximately exponential correlation function.

AB - The approach presented provides very good results in modelling non-Gaussian time series. It is validated by direct numerical simulation of bimodal and Nakagami distributed sequences with approximately exponential correlation function.

KW - Local linearization

KW - Non-linear autoregression

KW - PDF

KW - Stochastic differential equation

UR - https://www.scopus.com/pages/publications/0032730545

U2 - 10.1016/S0165-1684(98)00165-0

DO - 10.1016/S0165-1684(98)00165-0

M3 - Article

AN - SCOPUS:0032730545

SN - 0165-1684

VL - 72

SP - 61

EP - 68

JO - Signal Processing

JF - Signal Processing

IS - 2

ER -

On the generation of correlated time series with a given probability density function

Abstract

Keywords

ASJC Scopus subject areas

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