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

75 practiced

Discuss responsible AI and governance considerations specific to forecasting systems. Cover detection and mitigation of bias across regions or product lines, fairness when forecasts drive allocation decisions, data retention and privacy of training data, and operational governance practices such as model cards, periodic audits, and remediation processes.

MediumTechnical

80 practiced

Design a backtesting strategy for evaluating SKU-level weekly demand forecasts with two years of historical weekly data. Include how to choose rolling window lengths and number of folds, how to set forecast horizons that mirror production (for example 4 weeks), how to incorporate seasonality and promotions into folds, and which metrics you would report to product owners.

HardTechnical

82 practiced

Outline an approach to train LightGBM quantile regression models to predict multiple quantiles for hierarchical forecasts. Describe how to set up losses (pinball), options for training separate models per quantile versus multi-quantile strategies, how to detect and correct quantile crossing, and how to preserve calibration and coherence across hierarchy levels.

EasyTechnical

100 practiced

Define MAE, RMSE, and MAPE for forecast evaluation. For each metric explain sensitivity to outliers, interpretability for stakeholders, problems when actuals are near zero, and which business contexts favor one metric over another. Discuss scale-dependence and whether metrics are robust across SKUs with different volumes.

HardTechnical

76 practiced

For executive stakeholders who must decide on inventory and staffing using forecasts, design a strategy to communicate forecast uncertainty effectively. Recommend visualization types, concise narrative elements, risk thresholds, and simple decision rules that translate probabilistic outputs into clear operational actions.

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