time series

A sudden 22-point AUC jump is a gift and a warning. Junior candidates celebrate; strong ones get suspicious and know exactly which leakage checks to run before anything ships.

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

Cross-validation gives a reliable performance estimate, but the wrong scheme leaks data and lies. The signal 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 is 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, not 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 alerts fire all weekend and miss the real outage Monday. The signal is decomposing seasonality first, choosing the right detector, and tuning for alert fatigue. Here is the answer that survives production.

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

Anomaly detection sounds easy until seasonality, cold start, and alert fatigue hit. The signal is modeling the expected baseline (including weekly and daily cycles), choosing unsupervised methods for scarce labels, and tuning to avoid drowning users in false alarms. Here is the design.

Recognizing walking, driving, or a fall from a sensor stream is windowed time-series classification, usually on a battery-bound device. The signal is windowing, temporal smoothing, and the on-device constraints most candidates skip. Here is the design.

Forecasting one series is a textbook exercise; forecasting a million SKU-stores that must reconcile, with stockouts costing more than overstock, is the real interview. The strong answer goes global, hierarchical, and cost-aware.

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