statsmodels.tsa.x13.x13_arima_select_order¶

statsmodels.tsa.x13.
x13_arima_select_order
(endog, maxorder=(2, 1), maxdiff=(2, 1), diff=None, exog=None, log=None, outlier=True, trading=False, forecast_years=None, start=None, freq=None, print_stdout=False, x12path=None, prefer_x13=True)[source]¶ Perform automatic seaonal ARIMA order identification using x12/x13 ARIMA.
Parameters:  endog (arraylike, pandas.Series) – The series to model. It is best to use a pandas object with a
DatetimeIndex or PeriodIndex. However, you can pass an arraylike
object. If your object does not have a dates index then
start
andfreq
are not optional.  maxorder (tuple) – The maximum order of the regular and seasonal ARMA polynomials to examine during the model identification. The order for the regular polynomial must be greater than zero and no larger than 4. The order for the seaonal polynomial may be 1 or 2.
 maxdiff (tuple) – The maximum orders for regular and seasonal differencing in the
automatic differencing procedure. Acceptable inputs for regular
differencing are 1 and 2. The maximum order for seasonal differencing
is 1. If
diff
is specified thenmaxdiff
should be None. Otherwise,diff
will be ignored. See alsodiff
.  diff (tuple) – Fixes the orders of differencing for the regular and seasonal
differencing. Regular differencing may be 0, 1, or 2. Seasonal
differencing may be 0 or 1.
maxdiff
must be None, otherwisediff
is ignored.  exog (arraylike) – Exogenous variables.
 log (bool or None) – If None, it is automatically determined whether to log the series or not. If False, logs are not taken. If True, logs are taken.
 outlier (bool) – Whether or not outliers are tested for and corrected, if detected.
 trading (bool) – Whether or not trading day effects are tested for.
 forecast_years (int) – Number of forecasts produced. The default is one year.
 start (str, datetime) – Must be given if
endog
does not have date information in its index. Anything accepted by pandas.DatetimeIndex for the start value.  freq (str) – Must be givein if
endog
does not have date information in its index. Anything accapted by pandas.DatetimeIndex for the freq value.  print_stdout (bool) – The stdout from X12/X13 is suppressed. To print it out, set this to True. Default is False.
 x12path (str or None) – The path to x12 or x13 binary. If None, the program will attempt to find x13as or x12a on the PATH or by looking at X13PATH or X12PATH depending on the value of prefer_x13.
 prefer_x13 (bool) – If True, will look for x13as first and will fallback to the X13PATH environmental variable. If False, will look for x12a first and will fallback to the X12PATH environmental variable. If x12path points to the path for the X12/X13 binary, it does nothing.
Returns: results – A bunch object that has the following attributes:
 order : tuple The regular order
 sorder : tuple The seasonal order
 include_mean : bool Whether to include a mean or not
 results : str The full results from the X12/X13 analysis
 stdout : str The captured stdout from the X12/X13 analysis
Return type: Bunch
Notes
This works by creating a specification file, writing it to a temporary directory, invoking X12/X13 in a subprocess, and reading the output back in.
 endog (arraylike, pandas.Series) – The series to model. It is best to use a pandas object with a
DatetimeIndex or PeriodIndex. However, you can pass an arraylike
object. If your object does not have a dates index then