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
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
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.