Clean up and recompile

_freeze/index/execute-results/html.json

@@ -1,8 +1,8 @@
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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: [Mitch.O'[email protected]](mailto:Mitch.O'[email protected])\n* Elena Sanina\n* Xiaoqian Wang\n* Yangzhuoran (Fin) Yang\n* Zhixiang (Elvis) Yang\n\n## Consultations\n\n* Rob\n* Mitch\n* Elena\n* Elvis\n* Fin\n* Xiaoqian\n\n## Weekly schedule\n\n* Pre-recorded lectures: 1 hour per week ([Slides](https://github.com/robjhyndman/fpp3_slides))\n* In-person lectures: 9am Fridays, [Central 1 Lecture Theatre, 25 Exhibition Walk](https://maps.app.goo.gl/RKdmJq2tBfw8ViNT9).\n* Tutorials: 1.5 hours in class per week\n\n\n\n\n|Date   |Topic                               |Chapter                          |Assessments    |\n|:------|:-----------------------------------|:--------------------------------|:--------------|\n|26 Feb |[Introduction to forecasting and R](./week1.html)|[1. Getting started](https://OTexts.com/fpp3/intro.html)|               |\n|04 Mar |[Time series graphics](./week2.html)|[2. Time series graphics](https://OTexts.com/fpp3/graphics.html)|[Assignment 1](assignments/A1.qmd)|\n|11 Mar |[Time series decomposition](./week3.html)|[3. Time series decomposition](https://OTexts.com/fpp3/decomposition.html)|               |\n|18 Mar |[The forecaster's toolbox](./week4.html)|[5. The forecaster's toolbox](https://OTexts.com/fpp3/toolbox.html)|[Assignment 2](assignments/A2.qmd)|\n|25 Mar |[Exponential smoothing](./week5.html)|[8. Exponential smoothing](https://OTexts.com/fpp3/expsmooth.html)|               |\n|01 Apr |Mid-semester break                  |                                 |               |\n|08 Apr |[Exponential smoothing](./week6.html)|[8. Exponential smoothing](https://OTexts.com/fpp3/expsmooth.html)|[Assignment 3](assignments/A3.qmd)|\n|15 Apr |[ARIMA models](./week7.html)        |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|               |\n|22 Apr |[ARIMA models](./week8.html)        |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|               |\n|29 Apr |[ARIMA models](./week9.html)        |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|[Assignment 4](assignments/A4.qmd)|\n|06 May |[Multiple regression and forecasting](./week10.html)|[7. Time series regression models](https://OTexts.com/fpp3/regression.html)|               |\n|13 May |[Dynamic regression](./week11.html) |[10. Dynamic regression models](https://OTexts.com/fpp3/dynamic.html)|               |\n|20 May |[Dynamic regression](./week12.html) |[10. Dynamic regression models](https://OTexts.com/fpp3/dynamic.html)|[Retail Project](assignments/Project.qmd)|\n\n\n## Assessments\n\nFinal exam 60%, project 20%, other assignments 20%\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",
+    "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: [Mitch.O'[email protected]](mailto:Mitch.O'[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|Date   |Topic                               |Chapter                          |Assessments    |\n|:------|:-----------------------------------|:--------------------------------|:--------------|\n|26 Feb |[Introduction to forecasting and R](./week1.html)|[1. Getting started](https://OTexts.com/fpp3/intro.html)|               |\n|04 Mar |[Time series graphics](./week2.html)|[2. Time series graphics](https://OTexts.com/fpp3/graphics.html)|[Assignment 1](assignments/A1.qmd)|\n|11 Mar |[Time series decomposition](./week3.html)|[3. Time series decomposition](https://OTexts.com/fpp3/decomposition.html)|               |\n|18 Mar |[The forecaster's toolbox](./week4.html)|[5. The forecaster's toolbox](https://OTexts.com/fpp3/toolbox.html)|[Assignment 2](assignments/A2.qmd)|\n|25 Mar |[Exponential smoothing](./week5.html)|[8. Exponential smoothing](https://OTexts.com/fpp3/expsmooth.html)|               |\n|01 Apr |Mid-semester break                  |                                 |               |\n|08 Apr |[Exponential smoothing](./week6.html)|[8. Exponential smoothing](https://OTexts.com/fpp3/expsmooth.html)|[Assignment 3](assignments/A3.qmd)|\n|15 Apr |[ARIMA models](./week7.html)        |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|               |\n|22 Apr |[ARIMA models](./week8.html)        |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|               |\n|29 Apr |[ARIMA models](./week9.html)        |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|[Assignment 4](assignments/A4.qmd)|\n|06 May |[Multiple regression and forecasting](./week10.html)|[7. Time series regression models](https://OTexts.com/fpp3/regression.html)|               |\n|13 May |[Dynamic regression](./week11.html) |[10. Dynamic regression models](https://OTexts.com/fpp3/dynamic.html)|               |\n|20 May |[Dynamic regression](./week12.html) |[10. Dynamic regression models](https://OTexts.com/fpp3/dynamic.html)|[Retail Project](assignments/Project.qmd)|\n\n\n## Assessments\n\nFinal exam 60%, project 20%, other assignments 20%\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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_freeze/week1/execute-results/html.json

@@ -1,9 +1,11 @@
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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\n## What is forecasting?\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\n## Slides for seminar\n\n<iframe src='https://docs.google.com/gview?url=https://raw.githubusercontent.com/numbats/af/main/slides/week1.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://raw.githubusercontent.com/numbats/af/main/slides/week1.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n\n## In-class activities\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\n\n## Assignments\n\n* [Assignment 1](assignments/A1.qmd) is due on Friday 08 March.\n",
-    "supporting": [],
+    "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://raw.githubusercontent.com/numbats/af/main/slides/week1.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://raw.githubusercontent.com/numbats/af/main/slides/week1.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n\n## Seminar activities\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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index.qmd

@@ -21,7 +21,6 @@ options(knitr.kable.NA = '')
 * [**Mitchell O'Hara-Wild**](https://mitchelloharawild.com). Email: [Mitch.O'[email protected]](mailto:Mitch.O'[email protected])
 * Elena Sanina
 * Xiaoqian Wang
-* Yangzhuoran (Fin) Yang
 * Zhixiang (Elvis) Yang
 
 ## Consultations
@@ -30,14 +29,13 @@ options(knitr.kable.NA = '')
 * Mitch
 * Elena
 * Elvis
-* Fin
 * Xiaoqian
 
 ## Weekly schedule
 
-* Pre-recorded lectures: 1 hour per week ([Slides](https://github.com/robjhyndman/fpp3_slides))
-* In-person lectures: 9am Fridays, [Central 1 Lecture Theatre, 25 Exhibition Walk](https://maps.app.goo.gl/RKdmJq2tBfw8ViNT9).
+* Pre-recorded videos: approximately 1 hour per week [[Slides](https://github.com/robjhyndman/fpp3_slides)]
 * Tutorials: 1.5 hours in class per week
+* Seminars: 9am Fridays, [Central 1 Lecture Theatre, 25 Exhibition Walk](https://maps.app.goo.gl/RKdmJq2tBfw8ViNT9).
 
 
 ```{r}