Top 5 Time series Interview Questions
Browse 5 real-world technical and behavioral interview questions about Time series. Review scenarios, edge cases, and architectural best practices.
Design the ingestion path for sensor telemetry from a few hundred machines. Which decisions matter most?
Size the stream from stated assumptions, keep series identity narrow so cardinality stays bounded, choose sampling deliberately and downsample keeping min, max and count rather than means alone, tier retention, and buffer at the edge with a bounded queue and a rate-limited drain. Use this time series answer to show the decision, trade-off, and evidence rather than a memorised definition.
You forecast every store and also the national total, and the store forecasts do not sum to it. What do you do?
Forecast at whichever levels have signal, then reconcile so the numbers are coherent by construction. Bottom-up preserves detail but accumulates noise, top-down is stable but allocates by an assumed proportion, and optimal reconciliation projects independent forecasts onto the set that adds up.
One machine's clock is ninety seconds ahead of the rest of the plant. Which of your numbers are wrong, and how would you have found out?
Every derived figure that joins on time is affected, and none of them error. Duration and attribution metrics take the skew directly, so a shift with twelve stops mis-measures availability by nearly four points, and only storing both event time and ingestion time makes the offset visible.
Walk me through what you check for stationarity and seasonality, and what you do about what you find.
Plot the series first, then use a decomposition and an ADF or KPSS test to separate trend from seasonality from noise. Difference or transform away a trend, use Fourier terms when several seasonal periods coexist, and treat holidays and calendar structure as features rather than outliers.
Why does a random train-test split ruin a forecasting model?
A random train-test split ruins a forecasting model by leaking future observations into training. Use time-based holdouts or rolling-origin backtesting, and build every feature only from data available before the forecast timestamp. Use this time series answer to show the decision, trade-off, and evidence rather than a memorised definition.