8. Encountering issues in seasonal differencing¶

For certain time series, the seasonal differencing operation may fail:

Traceback (most recent call last):
File "<stdin>", line 1, in <module>
"Could not successfully fit a viable ARIMA model "
There are no more samples after a first-order seasonal differencing. See
http://alkaline-ml.com/pmdarima/seasonal-differencing-issues.html for a
more in-depth explanation and potential work-arounds.


In short, the seasonal differencing test has detected your time series could benefit from a non-zero seasonal differencing term, D, but your data is exhausted after differencing it by m. Basically, your dataset is too small to be differenced by m. You only have several options as a work-around here:

• Use a larger training set.
• Determine whether or not you’ve set the appropriate m. Should it be smaller? See Setting m for more information on the topic.
• Manually set D=0 in the pmdarima.arima.auto_arima() call. This is the least desirable solution, since it skips a step that could lead to a better model.

The best decision is always to use a larger training set, but sometimes that simply is not possible. Make sure to set trace to at least 1 in order to see the search progress, and to a value >1 to see the maximum trace logging available. If you still cannot diagnose why you are getting this error message, consider Filing a bug.