Time Series Analysis

Master time series decomposition into deterministic and stochastic components, learn mixed model formulations, and build practical forecasting workflows with rigorous mathematical foundations

4-6 hoursIntermediate to Advanced3 lessons

Learning Objectives

Understand decomposition of time series into deterministic and stochastic components
Master trend analysis and seasonal pattern extraction methods
Apply additive and multiplicative model formulations
Implement ARIMA modeling for stochastic components
Build forecasting workflows with mixed models
Analyze real-world US time series data and case studies

Quick Mathematical Reference

Core Decomposition Models

Additive Model:

X_t = T_t + S_t + \varepsilon_t

Where $T_t$ is trend, $S_t$ is seasonality, and $\varepsilon_t$ is the stochastic component.

Multiplicative Model:

X_t = T_t \times S_t \times \varepsilon_t

Used when seasonal effects scale with the level of the series.

Mixed Model:

X_t = T_t \times S_t + \varepsilon_t

Hybrid formulation allowing flexible interaction between components.

Core Learning Modules

📘

Beginner

Preface

Introduction to deterministic and stochastic components, trend definition, random term and mixed model overview.

Duration: 30-60 minutes • Lessons: 1

What you'll learn:

Deterministic vs Stochastic
Trend (T_t)
Random term (ε_t)
Additive & Multiplicative Models

Getting Started

Begin with the preface to understand the decomposition framework, then progress to deterministic and stochastic modules for modeling and forecasting.

Next Steps & Applications

Start Learning

Begin with the preface to understand decomposition framework and modeling principles.

Practice Examples

Apply decomposition methods to real US time series data and case studies.

Begin your journey

Start with Preface