Data & drivers
Forecasting new products and locations (the cold start)
A cold start is a forecast with almost no history of its own. You borrow shape from similar items or places, use leading signals, keep the horizon short, and replace the borrowed prior as soon as real sales show up.
Updated Aug 7, 2026·8 min read
The hole in the series
A cold start is a forecast with almost no history of its own. New flavor. New store. New region. The usual teacher — the series itself — has not shown up yet. You need a prior, a short horizon, and a plan to hand off.
Borrowed shape, then a hand-off
Analogs and hierarchies
Cluster on what ops already uses: price band, category, channel, trade area. Start from that seasonal shape. Scale by distribution and price. In a hierarchy, shrink the child toward the parent until the child has a spine. Top-down only is unfair to the SKU. Bottom-up only is noise. Reconciliation exists for a reason.
New stores undershoot then overshoot more often than the slide deck says. Copying last year’s hero location produces a confident ramp into a different parking lot. Keep the interval wide.
Leading signals
Traffic, waitlists, appointments, search, preorders — if they exist, they are how you nowcast a series that has not learned to speak yet. Treat them with the same known-ahead rule as any covariate. A waitlist you will only export on Friday cannot help Monday’s inference.
Handing off to its own history
The cold start is over when a model trained only on this series beats the analog on a fair backtest. That can be weeks for a fast web SKU and a couple of seasons for a slow spare part. Until then, do not pretend the borrowed path is a measurement. And do not keep the analog in the driver’s seat out of habit.
FAQ
- How do you forecast a product with no sales history?
- Cluster it with similar SKUs (price band, category, channel), start from their seasonal shape, scale by distribution and price, and tighten the horizon. Update weekly until you have a season of its own.
- What about a new store?
- Use nearby stores, trade-area data, and the chain's typical ramp. New stores often undershoot then overshoot. A wide interval is better than a confident ramp copied from last year's hero location.
- When is the cold start over?
- When a model trained only on this series beats the analog on a backtest. That can be weeks for a fast web SKU and a couple of seasons for a slow spare part.
Keep going
Guide
How much history do you need to forecast?
Enough to see the pattern you are betting on. A weekly seasonal business wants more than one year; an hourly series can learn a week of shape faster. Quality of the target beats another dusty decade of the wrong grain.
Guide
Zero-shot forecasting
Zero-shot means you get a forecast without training on your series first. A foundation model uses what it learned from many other series, then reads yours. Fine-tuning is the later step, once you have enough of your own data to make it worth the spend.
Guide
What is demand forecasting?
Demand forecasting estimates how much of a product or service people will want in a coming period, so you can buy, staff, and price before the rush — not after it has already walked in.
Use case
Retail demand forecasting
Anticipate demand by product, store, channel, and region before buying or allocation decisions are locked.
Use case
Food demand and waste forecasting
Predict demand for perishable products while accounting for shelf life, substitutions, events, and weather.