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• One of the main enhancements in the X-12- ARIMA program involves the use of a regARIMA model, a regression model with ARIMA (autoregressive integrated moving average) errors. Thus, the X-12-ARIMA program contains methods developed by both the US Census Bureau and Statistics Canada.

• Perform x13-arima analysis for monthly or quarterly data. The series to model. It is best to use a pandas object with a DatetimeIndex or PeriodIndex. However, you can pass an array-like object. If your object does not have a dates index then start and freq are not optional.

• If you downloaded X-13ARIMA-SEATS, you’ll need to rename the x13as.exe file to x12a.exe. If you downloaded X-12-ARIMA, no need to change any filenames. The sax12 package was technically designed to work with X-12-ARIMA and so looks for that particular program.

The four major components of the X-12-ARIMA program are regARIMA modeling, model diagnostics, seasonal adjustment that uses enhanced X-11 methodology, and post-adjustment diagnostics. Statistics Canada’s X-11 method fits an ARIMA model to the original series, and then uses the model forecasts to extend the original series.

X-13-ARIMA-SEATS's source code can be found on the Census Bureau's website. The default method for seasonal adjustment is based on the X-11 algorithm. It is assumed that the observations in a time series,

Perform x13-arima analysis for monthly or quarterly data. The series to model. It is best to use a pandas object with a DatetimeIndex or PeriodIndex. However, you can pass an array-like object. If your object does not have a dates index then start and freq are not optional.

For example, the following command returns the forecasts of the ARIMA model as a "ts" time series: Because the forecast.save = "forecasts" argument has not been specified in the model call, series re-evaluates the call with the 'forecast' spec enabled.