Teun
1
Hi guys,

I have an ARIMA-model that looks like this

```
txt<- auto.arima(training, xreg=reg1training, lambda="auto", stepwise=FALSE)
txt
#> Series: training
#> Regression with ARIMA(0,1,1)(0,1,1)[12] errors
#> Box Cox transformation: lambda= -0.1176994
#>
#> Coefficients:
#> ma1 sma1 xreg
#> -0.6507 -0.4964 0.1057
#> s.e. 0.0897 0.1514 0.0188
#> sigma^2 estimated as 0.09377: log likelihood=-15.75
#> AIC=39.49 AICc=40.16 BIC=48.19
```

But what if I want to make a new ARIMA-model from the same order.

I know the function

```
arimaorder(txt)
#> p d q P D Q Frequency
#> 0 1 1 0 1 1 12
```

and

```
txt$arma
#>[1] 0 1 0 1 12 1 1
```

Making the new model manually is therefore not difficult.

But how could I accomplish it automatically?

So the end result must be something like

```
arima(newdata, order=c(same as txt))
```

Thanks!

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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