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Data & drivers

Promos, weather, prices, leading indicators, features, and the cold-start problem when history is thin.

9 min

Forecasting with covariates

A covariate is an extra series the model is allowed to see — a promo flag, a temperature, a price. Used well, it explains swings the target's past cannot. Used as a dump of every column you have, it just overfits last quarter.

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8 min

Finding leading indicators

A leading indicator moves before the number you care about. Search queries before orders, bookings before departures, parts receipts before output. The lag is the product; correlation on the same day is just a mirror.

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9 min

Feature engineering for time series

Most of the work is turning a clock and a few raw series into lags, rolling stats, and calendar flags the model can chew. Do that on a schedule, with no future leaking into the past, or the backtest is theater.

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8 min

Forecasting new products and locations

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.

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8 min

Intermittent demand

Spare parts, rare claims, cyber events, slow B2B orders — the series is mostly zeros, then a jump. Average error metrics lie. You often care about hit rate and size when a hit happens, not a daily mean of 0.4 units.

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8 min

Driver discovery

A driver is a series that improves the forecast when you are allowed to see it — lagged, tested, and still available at 6am. Correlation on a heatmap is not discovery. Discovery is a backtest with the candidate in and out.

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8 min

Time series data quality

Most forecast failures are not the model. They are a DST shift, a file of zeros that looked like a miracle, sales used as demand after stockouts, or a timestamp that is actually an invoice date. Fix the clock before you race architectures.

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8 min

Price, promo, and lift

Lift is what happens to the path when you change a price or a promo you actually control. It is a scenario on a model that has seen similar moves — not a 2.4 elasticity copied from a textbook onto a fare class, a kWh tariff, or a SKU.

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Glossary in this topic

Covariate Exogenous variable Lag feature Rolling window Cold start Hierarchical forecasting Intermittent demand Feature engineering Leading indicator

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