> ## Documentation Index
> Fetch the complete documentation index at: https://alphacastio.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# X13 ARIMA Seasonal Adjustment
> Seasonally adjust series with X-13ARIMA-SEATS, with control over model order, outliers, log transformation and forecast horizon.
The **X13 ARIMA Seasonal Adjustment** step removes seasonal variation using X-13ARIMA-SEATS, the procedure maintained by the US Census Bureau. Unlike the prebuilt **Seasonally adjusted** option in [Apply Transform](/steps/transform), this step exposes the model parameters and can also output the trend and irregular components.
## Output columns
For each selected column, the step appends:
| Column | When |
| ---------------------- | ------------------------------------------------------------ |
| `{column} - sa` | Always. The seasonally adjusted series. |
| `{column} - trend` | When trendline output is enabled. The trend-cycle component. |
| `{column} - irregular` | When irregular output is enabled. The residual component. |
Original columns are left in place.
## Configuration
Pick the **columns to transform**; everything else sits behind **advanced options**, with defaults that work for most series.
| Field | Description |
| ----------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Columns to transform | The numeric columns to adjust. |
| Log Transformation | Whether to take logs. Required if you want to model multiplicative effects on a log scale. |
| Model | **Auto** lets X-13 identify the best model from the data. Otherwise give a 6-digit code — `011011` means an ARIMA (0 1 1)(0 1 1) specification, regular orders first, seasonal orders second. |
| Max Order — Regular / Seasonal Polynomial | Bounds for automatic model identification, previewed as `maxorder = (2, 1)`. Only used when the model is **Auto**. |
| Forecast periods | Number of forecast periods to produce. Defaults to 0. |
| Apply outlier detection | Whether outliers are tested for and corrected when detected. |
| Output trendline | Adds the `- trend` column. |
| Output irregular component | Adds the `- irregular` column. |
## Requirements and failure modes
Seasonal adjustment has real preconditions, and the step raises an error rather than returning a degraded result:
* **Minimum observations.** A series needs more than 36 observations, or more than 12 for quarterly data.
* **No zero or negative values.** Adjustment cannot be applied to series containing them.
* **One model per entity.** Each entity is adjusted independently. If adjustment fails for any entity the step reports which ones and stops, so a partial result is never published silently.
Adjustment runs per entity group and in parallel across columns, so adjusting many series at once is not much slower than adjusting one.
## Next steps
The one-click seasonal adjustment, without model control.
Resample to monthly or quarterly first — X-13 requires one of the two.