Forecasting Methods & Evaluation Library

A practical catalog of forecasting algorithms, error metrics, backtesting patterns, and model-selection guidance to move forecasts from experiments into repeatable operations.

Workbook

Forecasting & Planning Workbook

Interactive workbook with step-by-step problem framing, a data-preparation checklist, baseline forecast choices, seasonal decomposition cues, configurable backtesting plan, selectable error metrics (including CRPS for probabilistic forecasts), and a scenario-planning worksheet that links forecasts to concrete operational decisions (inventory, staffing, budgets). Responses are saved so teams can iterate, compare runs, and track follow-through.

Workbook
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Workflow

Forecasting & Planning — Validation & Backtest Workflow

An operational, step-by-step workflow to validate forecasting models, design and run backtests, select appropriate error metrics and horizons, generate probabilistic scenarios, and convert forecast outputs into S&OP, staffing, and inventory plans. Each step lists ownership, required artifacts, acceptance criteria, and common pitfalls so forecasts become reliable inputs for decisions.

Workflow
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Dashboard

Forecasting Backtest & Performance Dashboard

A practical monitoring dashboard that makes forecast quality visible across horizons, segments, models, and backtest windows. Includes horizon-aware error metrics, calibration and bias visualizations, ensemble comparisons, configurable backtest windows, alerts for regression, and prescriptive remediation actions with clear owners and next steps.

Dashboard
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Guide

Selecting Forecasting Algorithms: Practical, Operational Guide

Practical guidance to choose forecasting approaches based on data size, seasonality, horizon, and explainability needs. Includes a decision logic map, data preparation tips, when to prefer probabilistic forecasts, worked examples, common pitfalls, and an operational evaluation checklist.

Guide
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