Create an Autoregressive Integrated Moving Average (ARIMA) Process
Sets up the necessary backend for the ARIMA process.
ARIMA(ar = 1, i = 0, ma = 1, sigma2 = 1)
ar |
A |
i |
An |
ma |
A |
sigma2 |
A |
A variance is required since the model generation statements utilize randomization functions expecting a variance instead of a standard deviation like R.
An S3 object with called ts.model with the following structure:
AR x p, MA x q
sigma
Number of parameters
String containing simplified model
y desc replicated x times
Depth of parameters e.g. list(c(length(ar),length(ma),1) )
Guess starting values? TRUE or FALSE (e.g. specified value)
We consider the following model:
Δ^i X_t = ∑_{j = 1}^p φ_j Δ^i X_{t-j} + ∑_{j = 1}^q θ_j \varepsilon_{t-j} + \varepsilon_t
, where \varepsilon_t is iid from a zero mean normal distribution with variance σ^2.
James Balamuta
# 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)
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