gen_nswn: Generate Non-Stationary White Noise Process

In simts: Time Series Analysis Tools

Generate Non-Stationary White Noise Process

Description

This function allows to generate a non-stationary white noise process.

Usage

gen_nswn(n_total, title = NULL, seed = 135, ...)

Arguments

n_total	An integer indicating the length of the simulated non-stationary white noise process.
title	A string defining the name of the time series data.
seed	An integer defined for simulation replication purposes.
...	Additional parameters.

Value

A vector containing the non-stationary white noise process.

Note

This function generates a non-stationary white noise process whose theoretical maximum overlapping allan variance (MOAV) corresponds to the theoretical MOAV of the stationary white noise process. This example confirms that the allan variance is unable to distinguish between a stationary white noise process and a white noise process whose second-order behavior is non-stationary, as pointed out in the paper "A Study of the Allan Variance for Constant-Mean Non-Stationary Processes" by Xu et al. (IEEE Signal Processing Letters, 2017), preprint available: https://arxiv.org/abs/1702.07795.

Author(s)

Yuming Zhang

Examples

Xt = gen_nswn(n_total = 1000)
plot(Xt)

Yt = gen_nswn(n_total = 2000, title = "non-stationary 
white noise process", seed = 1960)
plot(Yt)

simts documentation built on Aug. 31, 2023, 5:07 p.m.