I want to seasonally adjust my time series, with obvious end-of-year seasonal effect (aggregate consumption).

For that purpose I used X-13ARIMA-SEATS from the `seasonal`

package, this is the call and `summary()`

of the model:

```
>library(seasonal)
>summary(seas(X, transform.function="none"))
Call:
seas(x = X,
transform.function = "none")
Coefficients:
Estimate Std. Error z value Pr(>|z|)
Constant 1415.7 109.3 12.95 < 2e-16 ***
LS2015.4 -2067.0 412.5 -5.01 5.43e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
SEATS adj. ARIMA: (0 0 0)(0 1 0) Obs.: 61 Transform: none
AICc: 929.6, BIC: 935.3 QS (no seasonality in final): 0
Box-Ljung (no autocorr.): 22.2 Shapiro (normality): 0.9512 *
```

My question is whether these results seem plausible, since I only apart from the constant, the `LS2015.4`

is a "level shifter" for what I can tell from the documentation and which is kind of visible in the fourth period of 2015. But particularly, I expected to have some `AR-Seasonal-04`

or `MA-Seasonal-04`

coefficient for the clearly visible mentioned component. Is something wrong? If so I suspect that a solution would be to manually set an non-zero seasonal ARIMA part, which indeed appear in the `topfivemods`

.

Thanks.