BYU-Idaho Time Series

Table of Contents
Resources
Chapter 1
- Overview
- Lessons & Homework
- Lesson 1
- Course Introduction
- Lesson 2
- Plots Trends, and Seasonal Variation
- Lesson 3
- Averages for Time Series
- Lesson 4
- Additive Models
- Lesson 5
- Multiplicative Models
Chapter 2
- Overview
- Lessons & Homework
- Lesson 1
- Covariance and Correlation
- Lesson 2
- Autocorrelation Concepts
- Lesson 3
- Exploration of Autocorrelation Concepts
Chapter 3
- Overview
- Lessons & Homework
- Lesson 1
- Leading Variables and Associated Variables
- Lesson 2
- Exponential Smoothing (EWMA)
- Lesson 3
- Holt-Winters Method (Additive Models) - Part 1
- Lesson 4
- Holt-Winters Method (Additive Models) - Part 2
- Lesson 5
- Holt-Winters Method (Multiplicative Models)
Chapter 4
- Overview
- Lessons & Homework
- Lesson 1
- White Noise and Random Walks - Part 1
- Lesson 2
- White Noise and Random Walks - Part 2
- Lesson 3
- Autoregressive (AR) Models
- Lesson 4
- Fitted AR Models
Chapter 5
- Overview
- Lessons & Homework
- Lesson 1
- Linear Models, GLS, and Seasonal Indicator Variables
- Lesson 2
- Transformations, Forecasting and Bias Correction
Chapter 6
- Overview
- Lessons & Homework
- Lesson 1
- Moving Average (MA) Models
- Lesson 2
- Autoregressive Moving Average (ARMA) Models
Chapter 7
- Overview
- Lessons & Homework
- Lesson 1
- Introduction to Non-stationary Models and Differencing
- Lesson 2
- Seasonal ARIMA Models
Appendix
- Harmonic Seasonal Variables
- Harmonic Seasonal Variables - Part 1
- Harmonic Seasonal Variables - Part 2
- ARCH and GARCH models
- ARCH and GARCH Models

On this page

Chapter 5

Overview
Lessons & Homework

Lessons & Homework

Regression

Chapter 5

Lessons

Lesson 1 - Linear Models, GLS, and Seasonal Indicator Variables
Lesson 2 - Forecasting, Inverse Transformation, and Bias Correction

Download Homework Assignments

homework_5_1.qmd

homework_5_2.qmd

Linear Models, GLS, and Seasonal Indicator Variables