Forecast demand

Goal: weekly unit sales per store for the next four weeks.

The query

FORECAST N TIMEFRAMES repeats the target window N times, back to back:

PREDICT SUM(sales.qty, 0, 7, days) FORECAST 4 TIMEFRAMES
FOR EACH stores.store_id

Timeframe 1 covers days (0, 7], timeframe 2 covers (7, 14], and so on.

Run it

ds = relativedb.from_dataframes(
    {"stores": stores, "sales": sales},
    links=[("sales", "store_id", "stores")])

df = ds.predict(query, anchor_time=t0)

The result has one row per store per timeframe. Forecasting routes to the regression checkpoint.

Notes

• The base window can be any unit: SUM(usage.count, 0, 1, days) FORECAST 28 TIMEFRAMES gives a daily 4-week forecast.
• Backtest by moving anchor_time into the past; the engine guarantees each forecast only saw data available at that anchor.
• A complete self-checking version lives at examples/industry/bizops_demand_forecast.py.