time series

A 22-point AUC jump is both an opportunity and a red flag. Weaker candidates cheer; sharp ones grow wary and know precisely which leakage checks belong before anything ships.

Time series breaks the usual ML assumptions: data is ordered and correlated, so random cross-validation silently leaks the future and inflates your score. What matters is decomposition, point-in-time features, and time-aware validation.

Cross-validation yields a reliable performance estimate, but the wrong scheme leaks data and misleads. What matters is matching the CV scheme to the data (stratified, grouped, time-series) and fitting preprocessing inside the fold.

The Kalman filter is optimal Bayesian tracking under linear-Gaussian assumptions, and it amounts to two steps repeated forever. The signal is explaining what the gain trades off and when the assumptions break. Here is the answer.

Prophet treats forecasting as curve-fitting trend plus seasonality plus holidays, rather than as a stochastic process. The signal is knowing why that design wins on business data and where it quietly fails. Here is the answer.

Threshold alarms page you all weekend yet sleep through Monday's actual outage. What interviewers reward is stripping out seasonality first, matching the detector to the anomaly, and tuning against alert fatigue. Here is the answer that holds up in production.

Days with zero events simply don't show up in an event table, so a 7-day average quietly averages the wrong 7 rows. A date spine makes the missing days explicit. Here is the pattern.

Anomaly detection seems simple until seasonality, cold start, and alert fatigue arrive. The signal is modeling the expected baseline (including weekly and daily cycles), picking unsupervised methods when labels are scarce, and tuning so you do not bury users in false alarms. Here is the design.

Detecting walking, driving, or a fall from a sensor stream is windowed time-series classification, typically on a battery-bound device. What matters is windowing, temporal smoothing, and the on-device constraints most candidates overlook. Here is the design.

Forecasting a single series is textbook work; forecasting a million SKU-stores that have to reconcile, where stockouts cost more than overstock, is what the interview actually tests. The strong answer is global, hierarchical, and cost-aware.

The observability infrastructure question. Interviewers want you to split the three telemetry types, choose storage that withstands massive write volume, and design alerting that fires fast without burying on-call in noise. Here is the pipeline with retention and cardinality math.