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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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.

MediumTechnical

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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

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You need to optimize inventory decisions under forecast uncertainty. Describe how to convert probabilistic demand forecasts into safety stock levels and reorder points. State assumptions required (lead time distribution, service level objective), derive the safety stock formula under normality, discuss non-normal distributions and skew, and outline how to validate the policy using historical simulation.

MediumTechnical

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Explain hierarchical forecasting and reconciliation. Compare bottom-up and top-down approaches and describe MinT (optimal reconciliation) at a high level. Provide a plan to reconcile forecasts in a retail hierarchy (SKU -> store -> region -> national) and how you would validate reconciliation results.

MediumTechnical

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Given a table daily_sales(store_id, sku_id, sale_date, sales_quantity), write Python (pandas) code to create lag features (lags 1, 7, 14), rolling means (7 and 30 days), and cyclic encodings for day-of-week and month. Explain how to avoid data leakage when generating features for training and how to prepare features for forecasting multi-step horizons.

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