37How do you approach a time-series forecasting problem, and what is special about validating it?▼mediumAmazonGoogleMeta2 replies◆ premiumTime 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.Open full answer →
117How does Prophet (and decomposable forecasting) work, and when does it beat or lose to ARIMA and gradient boosting?▼mediumMetaUberStripe2 replies◆ premiumProphet 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.Open full answer →
118How do you build anomaly detection for a streaming time series, and how do you handle seasonality and concept drift?▼hardNetflixUberMicrosoft2 replies◆ premiumThreshold 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.Open full answer →
11Design an anomaly detection system for a metric (e.g. cloud billing) with seasonality and cold start.▼hardAmazonMicrosoftGoogle1 replies○ sign inAnomaly 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.Open full answer →
