Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary Auto-Correlation Function

Dima Bykhovsky; Netanel Tochilovsky; Alexander Rudyak

doi:10.1109/ICECET58911.2023.10389427

Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary Auto-Correlation Function

Dima Bykhovsky
, Netanel Tochilovsky
, Alexander Rudyak

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Simulation of stationary random processes (time series) is an essential engineering tool for system prototyping, design, and optimization. To create a simulation, a randomly generated time series must have a pre-defined distribution and autocorrelation function (ACF). It is challenging to model non-Gaussian distributions as using a linear filter can alter the target distribution. To address this issue, we wrote a Python package that implements one of the prominent methods in the field of random process simulation. To the best of our knowledge, it is the first publicly available implementation of such a simulator in Python.

Original language	English
Title of host publication	International Conference on Electrical, Computer and Energy Technologies, ICECET 2023
Publisher	Institute of Electrical and Electronics Engineers
ISBN (Electronic)	9798350327816
DOIs	https://doi.org/10.1109/ICECET58911.2023.10389427
State	Published - 1 Jan 2023
Externally published	Yes
Event	2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023 - Cape Town, South Africa Duration: 16 Nov 2023 → 17 Nov 2023

Publication series

Name	International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

Conference

Conference	2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023
Country/Territory	South Africa
City	Cape Town
Period	16/11/23 → 17/11/23

Keywords

Nak-agami distribution
Python
auto-correlation function
probability distributions
simulation
stationary process
time series

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

Access to Document

10.1109/ICECET58911.2023.10389427

Cite this

Bykhovsky, D., Tochilovsky, N., & Rudyak, A. (2023). Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary Auto-Correlation Function. In International Conference on Electrical, Computer and Energy Technologies, ICECET 2023 (International Conference on Electrical, Computer and Energy Technologies, ICECET 2023). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICECET58911.2023.10389427

Bykhovsky, Dima ; Tochilovsky, Netanel ; Rudyak, Alexander. / Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary Auto-Correlation Function. International Conference on Electrical, Computer and Energy Technologies, ICECET 2023. Institute of Electrical and Electronics Engineers, 2023. (International Conference on Electrical, Computer and Energy Technologies, ICECET 2023).

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abstract = "Simulation of stationary random processes (time series) is an essential engineering tool for system prototyping, design, and optimization. To create a simulation, a randomly generated time series must have a pre-defined distribution and autocorrelation function (ACF). It is challenging to model non-Gaussian distributions as using a linear filter can alter the target distribution. To address this issue, we wrote a Python package that implements one of the prominent methods in the field of random process simulation. To the best of our knowledge, it is the first publicly available implementation of such a simulator in Python.",

keywords = "Nak-agami distribution, Python, auto-correlation function, probability distributions, simulation, stationary process, time series",

author = "Dima Bykhovsky and Netanel Tochilovsky and Alexander Rudyak",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023 ; Conference date: 16-11-2023 Through 17-11-2023",

year = "2023",

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Bykhovsky, D, Tochilovsky, N & Rudyak, A 2023, Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary Auto-Correlation Function. in International Conference on Electrical, Computer and Energy Technologies, ICECET 2023. International Conference on Electrical, Computer and Energy Technologies, ICECET 2023, Institute of Electrical and Electronics Engineers, 2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023, Cape Town, South Africa, 16/11/23. https://doi.org/10.1109/ICECET58911.2023.10389427

Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary Auto-Correlation Function. / Bykhovsky, Dima; Tochilovsky, Netanel; Rudyak, Alexander.
International Conference on Electrical, Computer and Energy Technologies, ICECET 2023. Institute of Electrical and Electronics Engineers, 2023. (International Conference on Electrical, Computer and Energy Technologies, ICECET 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Simulation of stationary random processes (time series) is an essential engineering tool for system prototyping, design, and optimization. To create a simulation, a randomly generated time series must have a pre-defined distribution and autocorrelation function (ACF). It is challenging to model non-Gaussian distributions as using a linear filter can alter the target distribution. To address this issue, we wrote a Python package that implements one of the prominent methods in the field of random process simulation. To the best of our knowledge, it is the first publicly available implementation of such a simulator in Python.

AB - Simulation of stationary random processes (time series) is an essential engineering tool for system prototyping, design, and optimization. To create a simulation, a randomly generated time series must have a pre-defined distribution and autocorrelation function (ACF). It is challenging to model non-Gaussian distributions as using a linear filter can alter the target distribution. To address this issue, we wrote a Python package that implements one of the prominent methods in the field of random process simulation. To the best of our knowledge, it is the first publicly available implementation of such a simulator in Python.

KW - Nak-agami distribution

KW - Python

KW - auto-correlation function

KW - probability distributions

KW - simulation

KW - stationary process

KW - time series

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M3 - Conference contribution

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T3 - International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

BT - International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

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Bykhovsky D, Tochilovsky N, Rudyak A. Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary Auto-Correlation Function. In International Conference on Electrical, Computer and Energy Technologies, ICECET 2023. Institute of Electrical and Electronics Engineers. 2023. (International Conference on Electrical, Computer and Energy Technologies, ICECET 2023). doi: 10.1109/ICECET58911.2023.10389427

Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary Auto-Correlation Function

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Keywords

ASJC Scopus subject areas

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