Learn/Course 07
Classical ML · weeks 9

Forecasting & temporal ML

Regional demand shifts through promotions, holidays, and drift.

Opening production brief

A measurable problem, before a model.

Users

Operators, product owners, and people affected by the forecasting decision.

Data

forecast origin, seasonal naive, lag features, rolling backtests with version, owner, event time, and availability contracts.

Constraints

Regional demand shifts through promotions, holidays, and drift. Every solution must state latency, cost, capacity, and fallback limits.

Success

Offline evidence plus a deployable resolution: Rolling validation, statistical baselines, boosted forecasts, and uncertainty bands.

CHAPTER 1

Frame and baseline

Turn the production problem into explicit data, metric, and baseline contracts.

  1. 01

    Forecast contracts and naive models

    Use forecast contracts and naive models to move the forecasting production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
  2. 02

    Trend, seasonality, and known-future signals

    Use trend, seasonality, and known-future signals to move the forecasting production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
CHAPTER 2

Build and stress

Formalize the machinery, test counterexamples, and expose system limits.

  1. 03

    Backtesting without time travel

    Use backtesting without time travel to move the forecasting production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
  2. 04

    Statistical and ML forecasters

    Use statistical and ml forecasters to move the forecasting production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
CHAPTER 3

Resolve and operate

Join model behavior to architecture, observability, rollout, and rollback.

  1. 05

    Intervals, drift, and forecast operations

    Use intervals, drift, and forecast operations to move the forecasting production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
  2. HW

    Regional demand forecast

    Build a 24-hour pipeline with naive, statistical, and boosted baselines plus interval forecasts.

    Rubric · staged hints · reference resolution
Prerequisite guidance

3 concepts make this faster.

Prerequisites are guidance, never hard gates.

C02EvaluationA metric contract, leakage-safe validation, calibration analysis, and experiment plan.C03Data & featuresData contracts, point-in-time features, backfills, quality gates, and lineage.C05Trees & boostingCalibrated random-forest and XGBoost baselines with cost-sensitive thresholds.
Ready to work the problem?

Forecast contracts and naive models

Start lesson 01