Merge remote-tracking branch 'origin/main'

_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\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\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\n\n\n\n[**R code used in seminar**](seminar_code.R)\n\n## Seminar activities\n\n\n\n\n\n\n\n1.  Produce an STL decomposition as follows\n\n    ```r\n    us_construction <- us_employment |>\n      filter(Title == \"Construction\", year(Month) > 1980)\n    dcmp <- us_construction |>\n      model(stl = STL(Employed ~ trend(window = 9) + season(window = 11)))\n    dcmp |> components() |> autoplot()\n    ```\n2. What happens as you change the values of the two `window` arguments?\n3. How does the seasonal shape change over time? *[Hint:&nbsp;Try&nbsp;plotting the seasonal component using `gg_season`.]*\n4. Can you produce a plausible seasonally adjusted series? *[Hint:&nbsp;`season_adjust` is returned by `components()`.]*\n\n\n## Assignments\n\n* [Assignment 2](../assignments/A2.qmd) is due on Sunday 24 March.\n",
-    "supporting": [],
+    "markdown": "---\ntitle: \"Week 3: Time series decomposition\"\n---\n\n::: {.cell}\n\n:::\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[**Solutions to Exercises**](ex3-sol.qmd)\n\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\n\n\n[**R code used in seminar**](seminar_code.R)\n\n## Seminar activities\n\n\n\n\n\n\n1.  Produce an STL decomposition as follows\n\n    ```r\n    us_construction <- us_employment |>\n      filter(Title == \"Construction\", year(Month) > 1980)\n    dcmp <- us_construction |>\n      model(stl = STL(Employed ~ trend(window = 9) + season(window = 11)))\n    dcmp |> components() |> autoplot()\n    ```\n2. What happens as you change the values of the two `window` arguments?\n3. How does the seasonal shape change over time? *[Hint:&nbsp;Try&nbsp;plotting the seasonal component using `gg_season`.]*\n4. Can you produce a plausible seasonally adjusted series? *[Hint:&nbsp;`season_adjust` is returned by `components()`.]*\n\n\n## Assignments\n\n* [Assignment 2](../assignments/A2.qmd) is due on Sunday 24 March.\n",
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_freeze/week4/index/execute-results/html.json

@@ -1,9 +1,11 @@
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-    "markdown": "---\ntitle: \"Week 4: The forecaster's toolbox\"\n---\n\n::: {.cell}\n\n:::\n\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\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\n1. Create a training set for household wealth (`hh_budget`) by withholding the last four years as a test set.\n2. Fit all the appropriate benchmark methods to the training set and forecast the periods covered by the test set.\n3. Compute the accuracy of your forecasts. Which method does best?\n4. Do the residuals from the best method resemble white noise?\n\n\n\n## Assignments\n\n* [Assignment 2](../assignments/A2.qmd) is due on Sunday 24 March.\n* [Assignment 3](../assignments/A3.qmd) is due on Sunday 14 April.\n",
-    "supporting": [],
+    "markdown": "---\ntitle: \"Week 4: The forecaster's toolbox\"\n---\n\n::: {.cell}\n\n:::\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\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\n\n\n[**R code used in seminar**](seminar_code.R)\n\n## Seminar activities\n\n\n\n\n\n1. Create a training set for household wealth (`hh_budget`) by withholding the last four years as a test set.\n2. Fit all the appropriate benchmark methods to the training set and forecast the periods covered by the test set.\n3. Compute the accuracy of your forecasts. Which method does best?\n4. Do the residuals from the best method resemble white noise?\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n\n\n## Assignments\n\n* [Assignment 2](../assignments/A2.qmd) is due on Sunday 24 March.\n* [Assignment 3](../assignments/A3.qmd) is due on Sunday 14 April.\n```\n\n\n:::\n:::\n",
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