[ML] Improvements to sparse count modelling #721
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edsavage
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Oct 8, 2019
tveasey
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This simplifies sparse count and sum modelling and migrates to always updating the time series model, but using a weight which decreases in proportion to the number of empty buckets. This means we simply smoothly transition to modelling non-empty buckets for sparse data.
I've also removed the correction to the probability which accounts for the fraction of non-empty buckets: we have the rare function anyway if this is the primary concern. Finally, I changed periodicity testing so that it approximates the old behaviour, i.e. it tends to ignoring empty buckets as their proportion increases.
Closes #696.