new ARIMA-model with old ARIMA order

Machine Learning and Modeling
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2

Here's an example.

library(forecast)

fit <- auto.arima(USAccDeaths)
fit
#> Series: USAccDeaths 
#> ARIMA(0,1,1)(0,1,1)[12] 
#> 
#> Coefficients:
#>           ma1     sma1
#>       -0.4303  -0.5528
#> s.e.   0.1228   0.1784
#> 
#> sigma^2 estimated as 102860:  log likelihood=-425.44
#> AIC=856.88   AICc=857.32   BIC=863.11

# Use same order, but new parameters
Arima(AirPassengers, order=arimaorder(fit)[1:3], seasonal=arimaorder(fit)[4:6])
#> Series: AirPassengers 
#> ARIMA(0,1,1)(0,1,1)[12] 
#> 
#> Coefficients:
#>           ma1     sma1
#>       -0.3087  -0.1074
#> s.e.   0.0890   0.0828
#> 
#> sigma^2 estimated as 137.5:  log likelihood=-507.5
#> AIC=1021   AICc=1021.19   BIC=1029.63

# Use same order and same parameters
Arima(AirPassengers, model=fit)
#> Series: AirPassengers 
#> ARIMA(0,1,1)(0,1,1)[12] 
#> 
#> Coefficients:
#>           ma1     sma1
#>       -0.4303  -0.5528
#> s.e.   0.0000   0.0000
#> 
#> sigma^2 estimated as 102860:  log likelihood=-531.12
#> AIC=1064.24   AICc=1064.28   BIC=1067.12

Created on 2021-03-05 by the reprex package (v1.0.0)

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