Phase 3: Production ML Practices · ~90 minutes · Python

Time Series Fundamentals

Past performance does predict future results -- if you check for stationarity first.

What you will learn

Decompose a time series into trend, seasonality, and residual components and test for stationarity
Implement lag features and rolling statistics to convert a time series into a supervised learning problem
Build a walk-forward validation framework that prevents future data from leaking into training
Explain why random train/test splits are invalid for time series and demonstrate the performance gap versus proper temporal splits

Introduction

Type: Build
Language: Python
Prerequisites: Earlier lessons in this course
Time: ~90 minutes

Objective

Learning objectives

Decompose a time series into trend, seasonality, and residual components and test for stationarity
Implement lag features and rolling statistics to convert a time series into a supervised learning problem
Build a walk-forward validation framework that prevents future data from leaking into training
Explain why random train/test splits are invalid for time series and demonstrate the performance gap versus proper temporal splits

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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