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

0 practiced

Your probabilistic forecaster's nominal 90% prediction intervals only capture 70% of actual observations (undercoverage). Describe a systematic approach to diagnose the source of undercoverage (model assumptions, heteroskedasticity, residual misspecification, covariate shift) and methods to fix it, including distributional modeling, heteroskedastic models, quantile models, bootstrap/conformal methods, and recalibration strategies.

EasyTechnical

0 practiced

Define probabilistic forecasting and give two practical methods to produce prediction intervals from deterministic models (for example: bootstrap residuals and quantile regression). Explain advantages of probabilistic forecasts for decision-making and give an example business decision where providing intervals or quantiles is necessary.

MediumTechnical

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Describe resource-aware hyperparameter tuning strategies for LSTM or Transformer forecasters at scale. Outline search spaces (layers, hidden units, learning rate, dropout), the use of early stopping, multi-fidelity approaches such as successive halving/Hyperband, Bayesian optimization with resource budget, and how to validate generalization across many series while keeping compute costs manageable.

MediumSystem Design

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Design a monitoring plan for production forecasting models. Specify what data and model metrics you would monitor (for example distribution of residuals, coverage of prediction intervals, seasonal-adjusted error rates), feature drift indicators, alert thresholds, automated responses (retrain, degrade to baseline, notify), and a triage process to determine whether issues are data, model, or concept drift related.

EasyTechnical

0 practiced

You receive a series with irregular timestamps and missing dates (for example sensor readings). List and justify preprocessing strategies before modeling: resampling to a regular grid, interpolation methods, imputation using domain knowledge, handling long gaps, and when to model irregular series directly (point-process models or sequence models that accept timestamp deltas).

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