ARIMA: Create an Autoregressive Integrated Moving Average (ARIMA)...

In simts: Time Series Analysis Tools

Create an Autoregressive Integrated Moving Average (ARIMA) Process

Description

Sets up the necessary backend for the ARIMA process.

Usage

ARIMA(ar = 1, i = 0, ma = 1, sigma2 = 1)

Arguments

ar	A vector or integer containing either the coefficients for \phi's or the process number p for the Autoregressive (AR) term.
i	An integer containing the number of differences to be done.
ma	A vector or integer containing either the coefficients for \theta's or the process number q for the Moving Average (MA) term.
sigma2	A double value for the standard deviation, \sigma, of the ARIMA process.

Details

A variance is required since the model generation statements utilize randomization functions expecting a variance instead of a standard deviation like R.

Value

An S3 object with called ts.model with the following structure:

process.desc

AR*p, MA*q

theta

\sigma

plength

Number of parameters

print

String containing simplified model

obj.desc

y desc replicated x times

obj

Depth of parameters e.g. list(c(length(ar),length(ma),1) )

starting

Guess starting values? TRUE or FALSE (e.g. specified value)

Note

We consider the following model:

\Delta^i X_t = \sum_{j = 1}^p \phi_j \Delta^i X_{t-j} + \sum_{j = 1}^q \theta_j \varepsilon_{t-j} + \varepsilon_t

, where \varepsilon_t is iid from a zero mean normal distribution with variance \sigma^2.

Author(s)

James Balamuta

Examples

# Create an ARMA(1,2) process
ARIMA(ar=1,2)
# Creates an ARMA(3,2) process with predefined coefficients.
ARIMA(ar=c(0.23,.43, .59), ma=c(0.4,.3))

# Creates an ARMA(3,2) process with predefined coefficients and standard deviation
ARIMA(ar=c(0.23,.43, .59), ma=c(0.4,.3), sigma2 = 1.5)

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