Demand forecasting estimates what customers will need over a defined future period. Its value depends on the decision it supports: buying stock, scheduling employees or planning capacity. A model that predicts tomorrow well may still be unsuitable for a purchasing decision made six weeks ahead.
Google Cloud’s November 2025 TimesFM announcement brought pretrained forecasting capabilities into BigQuery and AlloyDB. Easier access makes experimentation more practical, but it does not remove the need to test forecasts against a business baseline.
Start with the decision horizon
Define the item, location, time interval and lead time before selecting a model. Daily demand for a single store is a different problem from monthly demand across a region. Aggregation can conceal stockouts in individual locations even when the overall forecast looks accurate.
Check whether recorded sales represent demand. An empty shelf can produce zero sales despite strong customer interest. Mark those periods and document how the forecasting process handles them.
Compare against a seasonal baseline
A simple starting point predicts the same value as a comparable previous period, such as the same weekday in the previous week. Use a baseline that reflects the available history and business cycle.
Test both methods on later periods that were unavailable during development. Repeat this across several cutoffs rather than choosing one favorable month. Our guide to preventing data leakage explains why the information available at each prediction date matters.
Measure errors where they have consequences
Average absolute error is easy to interpret in business units. Also inspect systematic overprediction and underprediction by product or location. Percentage errors need care when actual demand is near zero.
For an illustrative staffing case, a forecast that repeatedly misses peak hours may be costly even if its daily average error is small. Compare the errors with service delays, inventory holding costs or missed orders, without assuming that every unit of error has the same consequence.
Use ranges and explicit exceptions
An uncertainty interval describes a range of possible outcomes under the model’s assumptions. Check how often observed demand falls inside it on held-out periods. A narrow interval is not useful if it routinely misses reality.
Flag launches, major promotions and missing data for review. Record overrides so that the team can later compare the original forecast with the adjusted one. Monitor changes after deployment using a model drift process.
Make the first project manageable
Choose one planning decision with consistent historical records and a measurable consequence. Explore no-code machine learning with NextBrain AI, and discuss your forecast horizon and data before expanding to every product or location.