Free checklist
Is your data ready for a forecasting model?
Twelve checks that decide whether a forecasting project is worth starting — the same ones I run before quoting an engagement. Fail three or more and a model isn’t your first problem.
What’s in it
Twelve checks. About four minutes.
Four of the twelve, so you know what you’re getting:
History depth — how many periods you need, and what to do when a SKU falls short.
Intermittency — when demand is too sparse for the model you were planning to use.
Hierarchy — whether your SKU, store, and channel grain reconcile without inventing numbers.
Drivers — which exogenous data earns its join: promo, weather, price, and which is noise.
Send it to my inbox.
One email with the checklist attached. No sequence, no drip — if you want to talk after reading it, you know where the calendar is.
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Opsis Data
Forecasting, causal inference, and automation for teams who need proof, not decks.
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Data Scientist · Analytics · Forecasting