Machine Learning and Forecasting Algorithms Questions

An in-depth coverage of machine learning methods used for forecasting and time-series prediction, including traditional time-series models (ARIMA, SARIMA, Holt-Winters), probabilistic forecasting techniques, and modern ML approaches (Prophet, LSTM/GRU, Transformer-based forecasters). Topics include feature engineering for seasonality and trend, handling non-stationarity and exogenous variables, model evaluation for time-series (rolling-origin cross-validation, backtesting, MAE/MAPE/RMSE), uncertainty quantification, and practical deployment considerations such as retraining, monitoring, and drift detection. Applies to forecasting problems in sales, demand planning, energy, finance, and other domains.

HardTechnical

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Macroeconomic covariates like fuel price or CPI are often monthly while sales are daily. Describe strategies to align these exogenous series with daily forecasts, how to include exogenous forecast uncertainty, and pitfalls such as lookahead bias when using future macro forecasts as features.

HardTechnical

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Explain the MinT (Minimum Trace) forecast reconciliation method mathematically. Provide the high-level linear algebra formulation that reconciles base forecasts into coherent hierarchical forecasts, explain how the covariance matrix of forecast errors is used, and outline algorithmic steps for implementation including shrinkage of covariance estimates for large hierarchies.

HardTechnical

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You inherit an ensemble of forecasting models but the ensemble's prediction intervals are overconfident and too narrow, which has caused inventory shortages. Describe a diagnostic approach to identify why intervals are too narrow, methods to recalibrate intervals (for example variance inflation, isotonic regression on quantile predictions, or re-training quantile regressors), and how to implement a permanent fix and monitoring to prevent recurrence.

EasyTechnical

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You need to present a quick, defensible baseline forecast for next quarter sales for a product with clear weekly seasonality. Describe at least two baseline approaches you would compute quickly (for example seasonal naive and moving average), explain advantages and limitations of each, and describe how you would present their uncertainty and relative performance to non-technical stakeholders.

HardSystem Design

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Design a production forecasting pipeline for daily SKU-level demand for an e-commerce company covering 100k SKUs across 3 regions, refreshing daily. Describe architecture components including data sources, feature store, training cluster, inference service, model registry/versioning, retraining policies, monitoring, latency goals for dashboard refresh, and cost trade-offs.

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