Data Science interview question · Time series and forecasting · senior level
How would you forecast daily orders for the next four weeks, and how do you know the forecast is any good?
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Why it is asked
Forecasts drive staffing, inventory and rider supply. The interviewer wants a baseline, an honest backtest and a range, not just the name of a model.
The answer
Start from a simple seasonal baseline, keep a more complex model only if it beats that baseline in an honest backtest, and report a range, not a single number.
1. Look at the data: trend, the weekly cycle, holidays and sales, outages, and level shifts. 2. Baselines. Seasonal naive means each future day equals the same weekday last week. It is often the best simple method, so every model must beat it. 3. Candidates: exponential smoothing with a weekly season (ETS, which updates level, trend and season as each day arrives), a regression on calendar and holiday features, or gradient boosting on lag features (orders 7, 14 and 28 days ago, rolling means). Trees cannot extrapolate a trend, so let them predict changes, not levels. 4. Backtest with rolling origins: cut the history at several dates, train only on data before each cut, forecast the next 28 days and score. A random split lets the model see the future. 5. Metrics: WAPE (total absolute error divided by total actual orders), because MAPE divides by each day's actual and becomes huge on days with zero or few orders; bias (are we always under?); and error by horizon week. 6. Uncertainty: prediction intervals from quantile models (which predict high and low values) or from backtest errors, because staffing plans use the high end.
In a simulation with two years of daily orders and six origins, copying the last day gave 15.2% WAPE, seasonal naive 4.9%, and ETS with a damped trend (a trend that slowly flattens) and weekly season 3.8%. ETS is worth keeping; a model that cannot beat 4.9% is not.
Then monitor live error and retrain on a schedule.
What the interviewer listens for
- Seasonal naive (same weekday last week) is the bar to beat.
- Rolling-origin backtest, never a random split.
- WAPE, bias and error by horizon, not MAPE on low-volume days.
- Give prediction intervals; plans use the high end.
Follow-ups the interviewer may ask
- How do you handle Diwali? Hint: holiday features built from earlier years, and a separate error check on holiday days.
- Why can gradient boosting on raw levels not forecast growth? Hint: trees only predict values seen in training, so model differences or ratios.
- How many backtest origins do you need? Hint: enough to cover different seasons, for example one every four weeks over the last six months.
Common mistakes
- A random train-test split on time series.
- No baseline, so nobody knows whether the model adds anything.
- Reporting one number with no interval.
Written by Roni Das, who builds distributed systems and AI systems for a living.
One question is not enough for the interview.
This is 1 of the 150 questions in the Data Science pack
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