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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* 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                |[2. Time series graphics](https://OTexts.com/fpp3/graphics.html)|[Assignment 1](assignments/A1.qmd)|\n|11 Mar |Time series decomposition           |[3. Time series decomposition](https://OTexts.com/fpp3/decomposition.html)|               |\n|18 Mar |The forecaster's toolbox            |[5. The forecaster's toolbox](https://OTexts.com/fpp3/toolbox.html)|[Assignment 2](assignments/A2.qmd)|\n|25 Mar |Exponential smoothing               |[8. Exponential smoothing](https://OTexts.com/fpp3/expsmooth.html)|               |\n|01 Apr |Mid-semester break                  |                                 |               |\n|08 Apr |Exponential smoothing               |[8. Exponential smoothing](https://OTexts.com/fpp3/expsmooth.html)|[Assignment 3](assignments/A3.qmd)|\n|15 Apr |ARIMA models                        |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|               |\n|22 Apr |ARIMA models                        |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|               |\n|29 Apr |ARIMA models                        |[9. ARIMA models](https://OTexts.com/fpp3/arima.html)|[Assignment 4](assignments/A4.qmd)|\n|06 May |Multiple regression and forecasting |[7. Time series regression models](https://OTexts.com/fpp3/regression.html)|               |\n|13 May |Dynamic regression                  |[10. Dynamic regression models](https://OTexts.com/fpp3/dynamic.html)|               |\n|20 May |Dynamic regression                  |[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/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\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/week2/index/execute-results/html.json

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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<iframe src='https://docs.google.com/gview?url=https://af.numbat.space/week/2slides.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://af.numbat.space/week/2slides.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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_freeze/week3/index/execute-results/html.json

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-    "markdown": "---\ntitle: \"Week 3: Time series decomposition\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* Transforming data to remove some sources of variation\n* Decomposing a time series into trend-cycle, seasonal and remainder components\n* Seasonal adjustment\n\n## Pre-class activities\n\nRead [Chapter 3 of the textbook](https://otexts.com/fpp3/decomposition.html) and watch all embedded videos\n\n## Exercises (on your own or in tutorial)\n\nComplete Exercises 6-11 from [Section 2.10 of the book](https://otexts.com/fpp3/graphics-exercises.html).\n\n\n\n## Slides for seminar\n\n<iframe src='https://docs.google.com/gview?url=https://af.numbat.space/week/3slides.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://af.numbat.space/week/3slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n## Seminar activities\n\n\n1. For the following series, find an appropriate Box-Cox transformation in order to stabilise the variance.\n\n    * United States GDP from `global_economy`\n    * Slaughter of Victorian “Bulls, bullocks and steers” in `aus_livestock`\n    * Victorian Electricity Demand from `vic_elec`.\n    * Gas production from `aus_production`\n\n2. Why is a Box-Cox transformation unhelpful for the `canadian_gas` data?\n\n3. Produce the following decomposition\n\n    ```r\n    canadian_gas |>\n      STL(Volume ~ season(window=7) + trend(window=11)) |>\n      autoplot()\n    ```\n\n4. What happens as you change the values of the two `window` arguments?\n\n5. How does the seasonal shape change over time? [Hint: Try plotting the seasonal component using `gg_season`.]\n\n6. Can you produce a plausible seasonally adjusted series? [Hint: `season_adjust` is one of the variables returned by `STL`.]\n\n\n\n## Assignments\n\n* [Assignment 2](../assignments/A2.qmd) is due on Friday 22 March.\n",
+    "markdown": "---\ntitle: \"Week 3: Time series decomposition\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* Transforming data to remove some sources of variation\n* Decomposing a time series into trend-cycle, seasonal and remainder components\n* Seasonal adjustment\n\n## Pre-class activities\n\nRead [Chapter 3 of the textbook](https://otexts.com/fpp3/decomposition.html) and watch all embedded videos\n\n## Exercises (on your own or in tutorial)\n\nComplete Exercises 6-11 from [Section 2.10 of the book](https://otexts.com/fpp3/graphics-exercises.html).\n\n\n\n## Slides for seminar\n\n<iframe src='https://docs.google.com/gview?url=https://af.numbat.space/week3/slides.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://af.numbat.space/week3/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n## Seminar activities\n\n\n1. For the following series, find an appropriate Box-Cox transformation in order to stabilise the variance.\n\n    * United States GDP from `global_economy`\n    * Slaughter of Victorian “Bulls, bullocks and steers” in `aus_livestock`\n    * Victorian Electricity Demand from `vic_elec`.\n    * Gas production from `aus_production`\n\n2. Why is a Box-Cox transformation unhelpful for the `canadian_gas` data?\n\n3. Produce the following decomposition\n\n    ```r\n    canadian_gas |>\n      STL(Volume ~ season(window=7) + trend(window=11)) |>\n      autoplot()\n    ```\n\n4. What happens as you change the values of the two `window` arguments?\n\n5. How does the seasonal shape change over time? [Hint: Try plotting the seasonal component using `gg_season`.]\n\n6. Can you produce a plausible seasonally adjusted series? [Hint: `season_adjust` is one of the variables returned by `STL`.]\n\n\n\n## Assignments\n\n* [Assignment 2](../assignments/A2.qmd) is due on Friday 22 March.\n",
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-    "markdown": "---\ntitle: \"Week 4: The forecaster's toolbox\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* Four benchmark forecasting methods that we will use for comparison\n* Fitted values, residuals\n* Forecasting with transformations\n\n## Pre-class activities\n\nRead [Chapter 5 of the textbook](https://otexts.com/fpp3/toolbox.html) and watch all embedded videos\n\n## Exercises (on your own or in tutorial)\n\nComplete Exercises 1-5, 9-10 from [Section 3.7 of the book](https://otexts.com/fpp3/decomposition-exercises.html).\n\n\n## Slides for seminar\n\n<iframe src='https://docs.google.com/gview?url=https://af.numbat.space/week/4slides.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://af.numbat.space/week/4slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n## Seminar activities\n\n\n\n * Produce forecasts using an appropriate benchmark method for household wealth (`hh_budget`). Plot the results using `autoplot()`.\n * Produce forecasts using an appropriate benchmark method for Australian takeaway food turnover (`aus_retail`). Plot the results using `autoplot()`.\n\n  * Compute seasonal naïve forecasts for quarterly Australian beer production from 1992.\n  * Test if the residuals are white noise. What do you conclude?\n\n * Create a training set for household wealth (`hh_budget`) by withholding the last four years as a test set.\n * Fit all the appropriate benchmark methods to the training set and forecast the periods covered by the test set.\n * Compute the accuracy of your forecasts. Which method does best?\n * Repeat the exercise using the Australian takeaway food turnover data (`aus_retail`) with a test set of four years.\n\n\n\n## Assignments\n\n* [Assignment 2](../assignments/A2.qmd) is due on Friday 22 March.\n* [Assignment 3](../assignments/A3.qmd) is due on Friday 12 April.\n",
+    "markdown": "---\ntitle: \"Week 4: The forecaster's toolbox\"\n---\n\n::: {.cell}\n\n:::\n\n\n## What you will learn this week\n\n* Four benchmark forecasting methods that we will use for comparison\n* Fitted values, residuals\n* Forecasting with transformations\n\n## Pre-class activities\n\nRead [Chapter 5 of the textbook](https://otexts.com/fpp3/toolbox.html) and watch all embedded videos\n\n## Exercises (on your own or in tutorial)\n\nComplete Exercises 1-5, 9-10 from [Section 3.7 of the book](https://otexts.com/fpp3/decomposition-exercises.html).\n\n\n## Slides for seminar\n\n<iframe src='https://docs.google.com/gview?url=https://af.numbat.space/week4/slides.pdf&embedded=true' width='100%' height=465></iframe>\n<a href=https://af.numbat.space/week4/slides.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n## Seminar activities\n\n\n\n * Produce forecasts using an appropriate benchmark method for household wealth (`hh_budget`). Plot the results using `autoplot()`.\n * Produce forecasts using an appropriate benchmark method for Australian takeaway food turnover (`aus_retail`). Plot the results using `autoplot()`.\n\n  * Compute seasonal naïve forecasts for quarterly Australian beer production from 1992.\n  * Test if the residuals are white noise. What do you conclude?\n\n * Create a training set for household wealth (`hh_budget`) by withholding the last four years as a test set.\n * Fit all the appropriate benchmark methods to the training set and forecast the periods covered by the test set.\n * Compute the accuracy of your forecasts. Which method does best?\n * Repeat the exercise using the Australian takeaway food turnover data (`aus_retail`) with a test set of four years.\n\n\n\n## Assignments\n\n* [Assignment 2](../assignments/A2.qmd) is due on Friday 22 March.\n* [Assignment 3](../assignments/A3.qmd) is due on Friday 12 April.\n",
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