Reverted to slides via Google docs to allow all devices to show them

_freeze/index/execute-results/html.json

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     "markdown": "---\ntitle: \"ETC3550/5550 Applied forecasting\"\n---\n\n\n\n\n## Lecturer/Chief Examiner\n\n* [**Rob J Hyndman**](https://robjhyndman.com). Email: [[email protected]](mailto:[email protected])\n\n## Tutors\n\n* [**Mitchell O'Hara-Wild**](https://mitchelloharawild.com). Email: [[email protected]](mailto:[email protected])\n* Elena Sanina\n* Xiaoqian Wang\n* Zhixiang (Elvis) Yang\n\n## Consultations\n\n* Rob\n* Mitch\n* Elena\n* Elvis\n* Xiaoqian\n\n## Weekly schedule\n\n* Pre-recorded videos: approximately 1 hour per week [[Slides](https://github.com/robjhyndman/fpp3_slides)]\n* Tutorials: 1.5 hours in class per week\n* Seminars: 9am Fridays, [Central 1 Lecture Theatre, 25 Exhibition Walk](https://maps.app.goo.gl/RKdmJq2tBfw8ViNT9).\n\n\n\n\n|Week   |Topic                               |Chapter                          |Assessments    |\n|:------|:-----------------------------------|:--------------------------------|:--------------|\n|26 Feb |[Introduction to forecasting and R](./week1/index.html)|[1. Getting started](https://OTexts.com/fpp3/intro.html)|               |\n|04 Mar |[Time series graphics](./week2/index.html)|[2. Time series graphics](https://OTexts.com/fpp3/graphics.html)|[Assignment 1](assignments/A1.qmd)|\n|11 Mar |[Time series decomposition](./week3/index.html)|[3. Time series decomposition](https://OTexts.com/fpp3/decomposition.html)|               |\n|18 Mar |[The forecaster's toolbox](./week4/index.html)|[5. The forecaster's toolbox](https://OTexts.com/fpp3/toolbox.html)|[Assignment 2](assignments/A2.qmd)|\n|25 Mar |[Exponential smoothing](./week5/index.html)|[8. Exponential smoothing](https://OTexts.com/fpp3/expsmooth.html)|               |\n|01 Apr |Mid-semester break                  |                                 |               |\n|08 Apr |[Exponential smoothing](./week6/index.html)|[8. Exponential smoothing](https://OTexts.com/fpp3/expsmooth.html)|[Assignment 3](assignments/A3.qmd)|\n|15 Apr |[ARIMA models](./week7/index.html)  |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|               |\n|22 Apr |[ARIMA models](./week8/index.html)  |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|               |\n|29 Apr |[ARIMA models](./week9/index.html)  |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|[Assignment 4](assignments/A4.qmd)|\n|06 May |[Multiple regression and forecasting](./week10/index.html)|[7. Time series regression models](https://OTexts.com/fpp3/regression.html)|               |\n|13 May |[Dynamic regression](./week11/index.html)|[10. Dynamic regression models](https://OTexts.com/fpp3/dynamic.html)|               |\n|20 May |[Dynamic regression](./week12/index.html)|[10. Dynamic regression models](https://OTexts.com/fpp3/dynamic.html)|[Retail Project](assignments/Project.qmd)|\n\n\n## Assessments\n\n* [Assignment 1](assignments/A1.qmd): 2%\n* [Assignment 2](assignments/A2.qmd): 6%\n* [Assignment 3](assignments/A3.qmd): 6%\n* [Assignment 4](assignments/A4.qmd): 6%\n* [Retail project](assignments/Project.qmd): 20%\n* Final exam: 60%\n\n## R package installation\n\nHere is the code to install the R packages we will be using in this unit.\n\n```r\ninstall.packages(c(\"tidyverse\",\"fpp3\", \"GGally\"), dependencies = TRUE)\n```\n",
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-    "markdown": "---\ntitle: \"Week 1: What is forecasting?\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* How to think about forecasting from a statistical perspective\n* What makes something easy or hard to forecast?\n* Using the `tsibble` package in R\n\n## Pre-class activities\n\n* Install R and RStudio on your personal computer. Instructions are provided at [https://otexts.com/fpp3/appendix-using-r.html](https://otexts.com/fpp3/appendix-using-r.html).\n* Read [Chapter 1 of the textbook](http://OTexts.com/fpp3/intro.html) and watch all embedded videos\n* Watch this video\n\n<iframe width=\"100%\" height=\"415\" src=\"https://www.youtube.com/embed/HNJYRf0mvxg?si=k0wfI3Sq68TPm4Ek\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n\n## Exercises (on your own or in tutorial)\n\nYour task this week is to make sure you are familiar with R, RStudio and the tidyverse packages. If you've already done ETC1010, then you may not need to do anything! But if you're new to R and the tidyverse, then you will need to get yourself up-to-speed.\n\nWork through the first five chapters of the **LearnR** tutorial at [learnr.numbat.space](https://learnr.numbat.space). Do as much of it as you think you need. For those students new to R, it is strongly recommended that you do all five chapters. For those who have previously used R, concentrate on the parts where you feel you are weakest.\n\n\n## Slides for seminar\n\n<embed src='https://af.numbat.space/week1/slides.pdf' type='application/pdf' width='100%' height=465></embed>\n<a href=https://af.numbat.space/week1/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n## Seminar activities\n\n\n1. Download `tourism.xlsx` from [`http://robjhyndman.com/data/tourism.xlsx`](http://robjhyndman.com/data/tourism.xlsx), and read it into R using `read_excel()` from the `readxl` package.\n2. Create a tsibble which is identical to the `tourism` tsibble from the `tsibble` package.\n3. Find what combination of `Region` and `Purpose` had the maximum number of overnight trips on average.\n4. Create a new tsibble which combines the Purposes and Regions, and just has total trips by State.\n\n\n\n## Assignments\n\n* [Assignment 1](../assignments/A1.qmd) is due on Friday 08 March.\n",
+    "markdown": "---\ntitle: \"Week 1: What is forecasting?\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* How to think about forecasting from a statistical perspective\n* What makes something easy or hard to forecast?\n* Using the `tsibble` package in R\n\n## Pre-class activities\n\n* Install R and RStudio on your personal computer. Instructions are provided at [https://otexts.com/fpp3/appendix-using-r.html](https://otexts.com/fpp3/appendix-using-r.html).\n* Read [Chapter 1 of the textbook](http://OTexts.com/fpp3/intro.html) and watch all embedded videos\n* Watch this video\n\n<iframe width=\"100%\" height=\"415\" src=\"https://www.youtube.com/embed/HNJYRf0mvxg?si=k0wfI3Sq68TPm4Ek\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n\n## Exercises (on your own or in tutorial)\n\nYour task this week is to make sure you are familiar with R, RStudio and the tidyverse packages. If you've already done ETC1010, then you may not need to do anything! But if you're new to R and the tidyverse, then you will need to get yourself up-to-speed.\n\nWork through the first five chapters of the **LearnR** tutorial at [learnr.numbat.space](https://learnr.numbat.space). Do as much of it as you think you need. For those students new to R, it is strongly recommended that you do all five chapters. For those who have previously used R, concentrate on the parts where you feel you are weakest.\n\n\n## Slides for seminar\n\n<iframe src='https://docs.google.com/gview?url=https://af.numbat.space/week1/slides.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://af.numbat.space/week1/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n## Seminar activities\n\n\n1. Download `tourism.xlsx` from [`http://robjhyndman.com/data/tourism.xlsx`](http://robjhyndman.com/data/tourism.xlsx), and read it into R using `read_excel()` from the `readxl` package.\n2. Create a tsibble which is identical to the `tourism` tsibble from the `tsibble` package.\n3. Find what combination of `Region` and `Purpose` had the maximum number of overnight trips on average.\n4. Create a new tsibble which combines the Purposes and Regions, and just has total trips by State.\n\n\n\n## Assignments\n\n* [Assignment 1](../assignments/A1.qmd) is due on Friday 08 March.\n",
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-    "markdown": "---\ntitle: \"Week 10: Multiple regression and forecasting\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* Useful predictors for time series forecasting using regression\n* Selecting predictors\n* Ex ante and ex post forecasting\n\n## Pre-class activities\n\nRead [Chapter 7 of the textbook](https://otexts.com/fpp3/regression.html) and watch all embedded videos\n\n## Exercises (on your own or in tutorial)\n\nComplete Exercises 11-17 from [Section 9.11 of the book](https://otexts.com/fpp3/??-exercises.html).\n\n\n## Slides for seminar\n\n<embed src='https://af.numbat.space/week10/slides.pdf' type='application/pdf' width='100%' height=465></embed>\n<a href=https://af.numbat.space/week10/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n## Assignments\n\n* [Retail Project](../assignments/Project.qmd) is due on Friday 24 May.\n",
+    "markdown": "---\ntitle: \"Week 10: Multiple regression and forecasting\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* Useful predictors for time series forecasting using regression\n* Selecting predictors\n* Ex ante and ex post forecasting\n\n## Pre-class activities\n\nRead [Chapter 7 of the textbook](https://otexts.com/fpp3/regression.html) and watch all embedded videos\n\n## Exercises (on your own or in tutorial)\n\nComplete Exercises 11-17 from [Section 9.11 of the book](https://otexts.com/fpp3/??-exercises.html).\n\n\n## Slides for seminar\n\n<iframe src='https://docs.google.com/gview?url=https://af.numbat.space/week10/slides.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://af.numbat.space/week10/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n## Assignments\n\n* [Retail Project](../assignments/Project.qmd) is due on Friday 24 May.\n",
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-    "markdown": "---\ntitle: \"Week 11: Dynamic regression\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* How to combine regression models with ARIMA models to form dynamic regression models\n* Dynamic harmonic regression to handle complex seasonality\n* Lagged predictors\n\n## Pre-class activities\n\nRead [Chapter 10 of the textbook](https://otexts.com/fpp3/dynamic.html) and watch all embedded videos\n\n## Exercises (on your own or in tutorial)\n\nComplete Exercises 1-7 from [Section 7.10 of the book](https://otexts.com/fpp3/regression-exercises.html).\n\n\n## Slides for seminar\n\n<embed src='https://af.numbat.space/week11/slides.pdf' type='application/pdf' width='100%' height=465></embed>\n<a href=https://af.numbat.space/week11/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n## Seminar activities\n\n\n\nRepeat the daily electricity example, but instead of using a quadratic function of temperature, use a piecewise linear function with the \"knot\" around 20 degrees Celsius (use predictors `Temperature` & `Temp2`). How can you optimize the choice of knot?\n\nThe data can be created as follows.\n\n```r\nvic_elec_daily <- vic_elec |>\n  filter(year(Time) == 2014) |>\n  index_by(Date = date(Time)) |>\n  summarise(\n    Demand = sum(Demand)/1e3,\n    Temperature = max(Temperature),\n    Holiday = any(Holiday)\n  ) |>\n  mutate(\n    Temp2 = I(pmax(Temperature-20,0)),\n    Day_Type = case_when(\n      Holiday ~ \"Holiday\",\n      wday(Date) %in% 2:6 ~ \"Weekday\",\n      TRUE ~ \"Weekend\"\n    )\n  )\n```\n\nRepeat but using all available data, and handling the annual seasonality using Fourier terms.\n\n\n\n## Assignments\n\n* [Retail Project](../assignments/Project.qmd) is due on Friday 24 May.\n",
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+    "markdown": "---\ntitle: \"Week 11: Dynamic regression\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* How to combine regression models with ARIMA models to form dynamic regression models\n* Dynamic harmonic regression to handle complex seasonality\n* Lagged predictors\n\n## Pre-class activities\n\nRead [Chapter 10 of the textbook](https://otexts.com/fpp3/dynamic.html) and watch all embedded videos\n\n## Exercises (on your own or in tutorial)\n\nComplete Exercises 1-7 from [Section 7.10 of the book](https://otexts.com/fpp3/regression-exercises.html).\n\n\n## Slides for seminar\n\n<iframe src='https://docs.google.com/gview?url=https://af.numbat.space/week11/slides.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://af.numbat.space/week11/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n## Seminar activities\n\n\n\nRepeat the daily electricity example, but instead of using a quadratic function of temperature, use a piecewise linear function with the \"knot\" around 20 degrees Celsius (use predictors `Temperature` & `Temp2`). How can you optimize the choice of knot?\n\nThe data can be created as follows.\n\n```r\nvic_elec_daily <- vic_elec |>\n  filter(year(Time) == 2014) |>\n  index_by(Date = date(Time)) |>\n  summarise(\n    Demand = sum(Demand)/1e3,\n    Temperature = max(Temperature),\n    Holiday = any(Holiday)\n  ) |>\n  mutate(\n    Temp2 = I(pmax(Temperature-20,0)),\n    Day_Type = case_when(\n      Holiday ~ \"Holiday\",\n      wday(Date) %in% 2:6 ~ \"Weekday\",\n      TRUE ~ \"Weekend\"\n    )\n  )\n```\n\nRepeat but using all available data, and handling the annual seasonality using Fourier terms.\n\n\n\n## Assignments\n\n* [Retail Project](../assignments/Project.qmd) is due on Friday 24 May.\n",
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-    "markdown": "---\ntitle: \"Week 12: Review\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* Review Assignment 1\n* Review initial case studies\n* Discuss exam\n\n## Pre-class activities\n\nCatch up on any exercises not yet done\n\n## Exercises (on your own or in tutorial)\n\nComplete Exercises 2-6 from [Section 10.7 of the book](https://otexts.com/fpp3/dynamic-exercises.html).\n\n\n## Slides for seminar\n\n<embed src='https://af.numbat.space/week12/slides.pdf' type='application/pdf' width='100%' height=465></embed>\n<a href=https://af.numbat.space/week12/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n\n## Post-class activities\n\n* Do any exercises not yet finished.\n* Complete past exams\n* Re-read the textbook\n* Listen again to all lectures\n\n\n\n\n## Assignments\n\n* [Retail Project](../assignments/Project.qmd) is due on Friday 24 May.\n",
+    "markdown": "---\ntitle: \"Week 12: Review\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* Review Assignment 1\n* Review initial case studies\n* Discuss exam\n\n## Pre-class activities\n\nCatch up on any exercises not yet done\n\n## Exercises (on your own or in tutorial)\n\nComplete Exercises 2-6 from [Section 10.7 of the book](https://otexts.com/fpp3/dynamic-exercises.html).\n\n\n## Slides for seminar\n\n<iframe src='https://docs.google.com/gview?url=https://af.numbat.space/week12/slides.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://af.numbat.space/week12/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n\n## Post-class activities\n\n* Do any exercises not yet finished.\n* Complete past exams\n* Re-read the textbook\n* Listen again to all lectures\n\n\n\n\n## Assignments\n\n* [Retail Project](../assignments/Project.qmd) is due on Friday 24 May.\n",
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-    "markdown": "---\ntitle: \"Week 2: Time series graphics\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* Different types of plots for time series including time plots, season plots, subseries plots, lag plots and ACF plots\n* The difference between seasonal patterns and cyclic patterns in time series\n* What is \"white noise\" and how to identify it.\n\n## Pre-class activities\n\nRead [Chapter 2 of the textbook](https://otexts.com/fpp3/graphics.html) and watch all embedded videos\n\n## Exercises (on your own or in tutorial)\n\nComplete Exercises 1-5 from [Section 2.10 of the book](https://otexts.com/fpp3/graphics-exercises.html).\n\n\n## Slides for seminar\n\n<embed src='https://af.numbat.space/week2/slides.pdf' type='application/pdf' width='100%' height=465></embed>\n<a href=https://af.numbat.space/week2/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n\n## Seminar activities\n\n\n\n\n\n1. We have introduced various functions for time series graphics include `autoplot()`, `gg_season()`, `gg_subseries()`, `gg_lag()` and `ACF`. Use these functions to explore the quarterly tourism data for the Snowy Mountains.\n\n   ```r\n   snowy <- tourism |> filter(Region == \"Snowy Mountains\")\n   ```\n\n   What do you learn?\n\n2. Which time plot corresponds to which ACF plot?\n\n::: {.cell}\n::: {.cell-output-display}\n![](index_files/figure-html/acf-quiz-1.png){width=100%}\n:::\n:::\n\n\n## Assignments\n\n* [Assignment 1](../assignments/A1.qmd) is due on Friday 08 March.\n* [Assignment 2](../assignments/A2.qmd) is due on Friday 22 March.\n",
+    "markdown": "---\ntitle: \"Week 2: Time series graphics\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* Different types of plots for time series including time plots, season plots, subseries plots, lag plots and ACF plots\n* The difference between seasonal patterns and cyclic patterns in time series\n* What is \"white noise\" and how to identify it.\n\n## Pre-class activities\n\nRead [Chapter 2 of the textbook](https://otexts.com/fpp3/graphics.html) and watch all embedded videos\n\n## Exercises (on your own or in tutorial)\n\nComplete Exercises 1-5 from [Section 2.10 of the book](https://otexts.com/fpp3/graphics-exercises.html).\n\n\n## Slides for seminar\n\n<iframe src='https://docs.google.com/gview?url=https://af.numbat.space/week2/slides.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://af.numbat.space/week2/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n\n## Seminar activities\n\n\n\n\n\n1. We have introduced various functions for time series graphics include `autoplot()`, `gg_season()`, `gg_subseries()`, `gg_lag()` and `ACF`. Use these functions to explore the quarterly tourism data for the Snowy Mountains.\n\n   ```r\n   snowy <- tourism |> filter(Region == \"Snowy Mountains\")\n   ```\n\n   What do you learn?\n\n2. Which time plot corresponds to which ACF plot?\n\n::: {.cell}\n::: {.cell-output-display}\n![](index_files/figure-html/acf-quiz-1.png){width=100%}\n:::\n:::\n\n\n## Assignments\n\n* [Assignment 1](../assignments/A1.qmd) is due on Friday 08 March.\n* [Assignment 2](../assignments/A2.qmd) is due on Friday 22 March.\n",
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