Open mathrezende opened 1 year ago
Sorry for the delayed response. Yes, transform.function = "none"
should do the trick:
library(seasonal)
m <- seas(AirPassengers, transform.function = "none")
summary(m)
#>
#> Call:
#> seas(x = AirPassengers, transform.function = "none")
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> Constant 30.62077 4.60956 6.643 3.08e-11 ***
#> Leap Year 11.32104 3.43088 3.300 0.000968 ***
#> Weekday -0.90361 0.17787 -5.080 3.77e-07 ***
#> Easter[1] 6.89372 1.80972 3.809 0.000139 ***
#> AR-Nonseasonal-01 0.81929 0.04903 16.709 < 2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> SEATS adj. ARIMA: (1 0 0)(0 1 0) Obs.: 144 Transform: none
#> AICc: 993.4, BIC: 1010 QS (no seasonality in final): 0
#> Box-Ljung (no autocorr.): 29.2 Shapiro (normality): 0.984
I am using the seas function to run a X13 Arima on a time series. But when I call the seasonal, trend and irregular component I see that the seas function is providing me a multiplicative decomposition, but I want an additive decomposition.
Reading other issues, such as https://github.com/christophsax/seasonal/issues/225 I see that when the "transform.function" argument is changed from log to none, the algorithm changes the multiplicative to the additive decomposition.
Is there a way to specify the type of decomposition I want in the seas function or it is automatic?
Thanks!