Forecasting Methods & Evaluation Library

Compare algorithms, choose metrics, design realistic backtests, and prepare forecasts that inform operational decisions — not just reports.

Why this library matters

Forecasts guide staffing, inventory, budgeting, and risk planning. Yet many forecasting projects stall because model evaluation focuses on in-sample fit or a single error number instead of asking: Will this forecast behave under production conditions and meaningfully improve decisions? This resource collects practical methods, evaluation patterns, and operational checks so teams can move from promising prototypes to dependable forecasts.

What you'll understand and be able to do

After exploring the library you will be able to:

• Compare simple baselines (e.g., naive, seasonal averages) with statistical and machine-learning models, and understand when complexity adds value.
• Select error metrics that match business goals and forecast horizons (e.g., MAPE vs. MAE vs. quantile loss) and avoid misleading comparisons.
• Design backtests that reduce leakage and mimic production timing, including rolling-origin and blocked cross-validation patterns.
• Use model-selection workflows that balance accuracy, stability, interpretability, and operational cost.
• Translate forecast outputs into scenarios and operational actions that respect lead times, capacity, and uncertainty.

Who benefits

This library is useful for analysts, demand planners, operations managers, finance leads, data scientists, consultants, and leaders in small businesses, service providers, manufacturers, healthcare organizations, nonprofits, and education. Examples:

• A retail planner choosing between a seasonal-trend decomposition and a gradient-boosted model for weekly store demand.
• A hospital operations manager validating probabilistic staffing forecasts against real shift patterns and lead times.
• A manufacturer testing replenishment rules linked to probabilistic forecasts and vendor lead-time constraints.
• A nonprofit forecasting donations and scenario-planning budgets across multiple campaign horizons.

How the collection is organized

The library groups resources to support a complete validation and deployment workflow rather than isolated algorithms:

• Method catalog — clear descriptions of common algorithms and when they fit: naïve, exponential smoothing, ARIMA, state-space models, tree- and ensemble-based learners, and probabilistic approaches.
• Evaluation & metrics — guidance on choosing and interpreting error metrics by horizon, scale, and business impact (including when to use quantile or interval scoring).
• Backtesting patterns — templates and practices for realistic backtests: rolling-origin, blocked validation, and leak-resistant holdouts.
• Operational checks — sanity checks, calibration tests, and deployment readiness steps (e.g., monitoring, drift detection, and fall-back baselines).
• Practical artifacts — Forecasting & Planning Workbook, Validation & Backtest Workflow, Backtest & Performance Dashboard, and a practical guide to selecting algorithms to help you run experiments and capture reproducible results.

Practical advice — a few starting rules

Begin with a strong baseline and simple models; only accept added complexity when it improves business-relevant metrics in realistic backtests. Match metrics to decisions (inventory risk vs. staffing overrun). Test across multiple horizons, and expose uncertainty rather than hiding it in single-point forecasts. Finally, validate assumptions about seasonality, promotions, and external drivers before deploying.

Connecting forecasts to decisions

Forecasts are useful when they change actions. Use the library to link predictive outputs to planners' rules: reorder points, safety stock, shift scheduling, or budget scenarios. The included dashboard and workbook make it easier to capture evaluation results and discuss trade-offs with stakeholders.