ClassroomThe classroom
Back to the lesson libraryMECHANISM · 14 MIN
M13.1 CONNECT THE MECHANISM

Predict the future without borrowing information from it

A café has to order tomorrow's milk tonight. Beat "same as last week" at forecasting its rush hours, and learn why shuffling the data makes any forecast look brilliant.

LESSON OVERVIEW14 min lesson

Lesson overview

A café has to order tomorrow's milk tonight. Beat "same as last week" at forecasting its rush hours, and learn why shuffling the data makes any forecast look brilliant.

What you’ll explore

  • Time-series forecasting uses ordered observations, horizons, and temporal features; seasonal baselines and rolling evaluation expose leakage and changing behavior.
GO TO THE SOURCE

Original explanations, connected to the research.

Forecasting: Principles and Practice, 3rd edition, §5.2 Some simple forecasting methods (Hyndman & Athanasopoulos, 2021)Forecasting: Principles and Practice, 3rd edition, §5.10 Time series cross-validation (Hyndman & Athanasopoulos, 2021)Dive into Deep Learning — authors’ open textbookAn Introduction to Statistical Learning — authors’ materials
Suggest a correction

A precise note can make an explanation better.

Choose the scene and describe what needs attention. Download a feedback file to share through a channel you already use. This page does not send feedback or connect you with a reviewer.

The file includes this note, the scene title, and lesson metadata. Your saved progress and quiz responses are excluded. Download before leaving or reloading to keep your note.