Handouts/Notes, and R Markdowns for FIN 335 - Forecasting Methods

DISCLAIMER: The R Markdown files are expanded versions of the online text "Forecasting Principles and Practice" by Rob Hyndman and George Athanasopoulos from Monash University. These R Markdown files contain much of the body of their online text and use several of the authors' examples. Thus much of the writing found in them is theirs. These files do contain many more examples and provide more detailed R code for analyzing time series data. I have also added expanded discussion of some of the topics, mainly through the additional examples I have added.

Chapter 1 - Getting Started

• Introductory Powerpoint

• Chapter 1 - Getting Started (Chapter 1 FPP2 - R Markdown)

• Chapter 1 - Getting Started (Chapter 1 FPP2 - .docx)

• Guidelines for Assignments

Chapter 2 - Time Series Graphics

• Chapter 2 - Time Series Graphics (R Markdown)

• Chapter 2 - Time Series Graphics (.docx)

• Chapter 2 - Supplemental Handout (Additional Examples of Chapter 2 Content - R Markdown)

• Chapter 2 - Supplemental Handout (Additional Examples of Chapter 2 Content - .docx)

Chapter 3 - The Forecasters Toolbox

• Section 3.1 - Simple Forecasting Methods (R Markdown)

• Section 3.1 - Simple Forecasting Methods (.docx)

• Sections 3.2 and 3.3 - Time Series Adjustments & Transformations and Residual Diagnostics (R Markdown)

• Sections 3.2 and 3.3 - Time Series Adjustments & Transformations and Residual Diagnostics (.docx)

• Sections 3.4 and 3.5 - Evaluating Forecast Accuracy and Prediction Intervals (R Markdown)

• Sections 3.4 and 3.5 - Evaluating Forecast Accuracy and Prediction Intervals (.docx)

Chapter 5 - Linear Models

• Sections 5.1 - 5.4 - Simple and Multiple Regression Models (R Markdown) (Word .docx Version)

• Sections 5.5 - 5.9 - More on Regression and Forecasting with Regression Models (R Markdown) (Word .docx Version)

Chapter 6 - Time Series Decomposition

• Sections 6.1, 6.3 - 6.5 - Seasonal Decomposition Methods and Examples (R Markdown) (Word Version)

  VIDEO LECTURES

• Chapter 6 - remaining content Part 1
  
  
• Chapter 6 - remaining content Part 2

Chapter 7 - Exponential Smoothing

• Section 7.1 - Simple Exponential Smoothing (SES) (R Markdown) (Word Version)

• Section 7.2-7.3 - Holt's Trend Methods (R Markdown) (Word Version)

• Section 7.4 - 7.7 - Taxonomy of Exponential Smooth Models and ETS Models (R Markdown) (Word Version)

  
  VIDEO LECTURES:

• Chapter 7 - Section 7.1 - Simple Exponential Smoothing (SES)
  
  
• Chapter 7 - Sections 7.2 - 7.3 - Holt's Smoothing Methods
  
  
• Chapter 7 - Sections 7.4 - 7.7 - ETS methods (part 1)
  
  
• Chapter 7 - Sections 7.4 - 7.7 - ETS methods (part 2)

Chapter 8 - ARIMA Models

• Sections 8.0 - 8.1 - Intro to ARIMA and Stationarity & Differencing (R Markdown) (Word Version)

• Sections 8.2 - 8.5 (with discussion of 8.6 and 8.7 in video) (R Markdown) (Word Version)

• Section 8.9 - Seasonal ARIMA (R Markdown) (Word Version)

VIDEO LECTURES:

• Chapter 8 - Sections 8.0 - 8.1 - Intro to ARIMA, Stationarity, and Differencing (part 1)
  
  
• Chapter 8 - Sections 8.0 - 8.1 - Intro to ARIMA, Stationarity, and Diffierencing (part 2)
  
  
• Chapter 8 - Section 8.2 - Differencing and the Backshift Operator (B)
  
  
• Chapter 8 - Sections 8.3 - 8.4
  
  
• Chapter 8 - Sections 8.6 & 8.7

Chapter 9 - Neural Network Models for Time Series

• Section 11.3 in Hyndman text (R Markdown) (Word Version)

VIDEO LECTURE:

• Chapter 11 - Section 11.3 - Sinple Neural Networks in Forecasting

Final Project Example - Seasonal Time Series Analysis (R Markdown and Word version)

Final Project Example - Nonseasonal Time Series Analysis (R Markdown and Word Version)