I'm running a Kalman LLP5 algorithm within the MLTK to predict application crashes and account for trend and seasonality.
My search is:
| timechart span=1d sum(VOLUME) | predict "sum(VOLUME)" as prediction algorithm="LLP5" future_timespan="365" holdback="0" period=365 lower"95"=lower"95" upper"95"=upper"95" | `forecastviz(365, 0, "sum(VOLUME)", 95)`
However, as you can see, the algorithm predicts clear outliers just because they happened at the same time last year.
Is there a way to filter this out? And additionally, once it is filtered out, can I set an alert to tell me if the trend in the future does not follow this predicted trend or if there are outliers that fall well outside this confidence interval?
