Gaussian ARMA models in the ts.extend package

TL;DR

The paper presents the ts.extend R package that provides tools for working with stationary Gaussian ARMA models, including density, distribution, simulation, spectral analysis, and periodicity testing.

Contribution

It introduces new functions for Gaussian ARMA models in R, enabling easier computation, simulation, and spectral analysis of time-series data.

Findings

Package allows simulation of Gaussian ARMA time-series with conditional or marginal elements.

Provides functions for spectral intensity computation and permutation-spectrum testing.

Facilitates detection of periodic signals in time-series data.

Abstract

This paper introduces and describes the R package ts.extend, which adds probability functions for stationary Gaussian ARMA models and some related utility functions for time-series. We show how to use the package to compute the density and distributions functions for models in this class, and generate random vectors from this model. The package allows the user to use marginal or conditional models using a simple syntax for conditioning variables and marginalised elements. This allows users to simulate time-series vectors from any stationary Gaussian ARMA model, even if some elements are conditional values or omitted values. We also show how to use the package to compute the spectral intensity of a time-series vector and implement the permutation-spectrum test for a time-series vector to detect the presence of a periodic signal.