Improved week 3

Author: GeorgeAthana

_freeze/week2/index/execute-results/html.json

@@ -2,7 +2,7 @@
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-    "markdown": "---\ntitle: \"Week 2: Time series graphics\"\n---\n\n::: {.cell}\n\n:::\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## Tutorial exercises\n\n-   Complete Exercises 1-4 from [Section 2.10 of the book](https://otexts.com/fpp3/graphics-exercises.html).\n-   We completed Exercise 5 in last week's lectorial.\n-   [Tutorial learning objectives](tute-lo.qmd).\n\n## Exercise solutions\n\n::: {.callout appearance=\"minimal\"}\n<i class=\"bi bi-download\"></i> [Week 2 Tutorial Solution.html](Week2.html){download=\"Week 2 Tutorial Solution.html\"}\n:::\n\n\n## Pre-seminar activities\n\n-   Read [Chapter 2 of the textbook](https://otexts.com/fpp3/graphics.html) and watch all embedded videos\n\n\n\n\n\n## Slides for seminar\n\n<embed src='/week2/week2.pdf' type='application/pdf' width='100%' height=465></embed>\n<a href=/week2/week2.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n\n\n\n## Lectorial activities\n\n\n\n\n\n\n\n1. We have introduced various functions for time series graphics including `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\n3.  You can compute the daily changes in the Google stock price in 2018 using the code below. Do the daily changes look like white noise?\n\n\\fontsize{11.5}{15}\\sf\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndgoog <- gafa_stock |>\n  filter(Symbol == \"GOOG\", year(Date) >= 2018) |>\n  mutate(trading_day = row_number()) |>\n  update_tsibble(index=trading_day, regular=TRUE) |>\n  mutate(diff = difference(Close))\n```\n:::\n\n\\fontsize{14}{16}\\sf\n\n    \n\n\n\n\n\n## Seminar code\n\n::: {.callout appearance=\"minimal\"}\n<i class=\"bi bi-download\"></i> [week2.R](week2.R){download=\"week2.R\"}\n:::\n\n\n\n\n\n\n\n## Assignments\n\n* [IA1](../assignments/A1.qmd) is due on Monday 10 March.\n* [GA1](../assignments/G1.qmd) is due on Monday 24 March.\n",
+    "markdown": "---\ntitle: \"Week 2: Time series graphics\"\n---\n\n::: {.cell}\n\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## Tutorial exercises\n\n-   Complete Exercises 1-4 from [Section 2.10 of the book](https://otexts.com/fpp3/graphics-exercises.html).\n-   We completed Exercise 5 in last week's lectorial.\n-   [Tutorial learning objectives](tute-lo.qmd).\n\n## Exercise solutions\n\n::: {.callout appearance=\"minimal\"}\n<i class=\"bi bi-download\"></i> [Week 2 Tutorial Solution.html](Week2.html){download=\"Week 2 Tutorial Solution.html\"}\n:::\n\n\n## Pre-seminar activities\n\n-   Read [Chapter 2 of the textbook](https://otexts.com/fpp3/graphics.html) and watch all embedded videos\n\n\n\n\n## Slides for seminar\n\n<embed src='/week2/week2.pdf' type='application/pdf' width='100%' height=465></embed>\n<a href=/week2/week2.pdf class='badge badge-small badge-red'>Download pdf</a>\n\n\n\n## Lectorial activities\n\n\n\n\n\n\n1. We have introduced various functions for time series graphics including `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\n3.  You can compute the daily changes in the Google stock price in 2018 using the code below. Do the daily changes look like white noise?\n\n\\fontsize{11.5}{15}\\sf\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndgoog <- gafa_stock |>\n  filter(Symbol == \"GOOG\", year(Date) >= 2018) |>\n  mutate(trading_day = row_number()) |>\n  update_tsibble(index=trading_day, regular=TRUE) |>\n  mutate(diff = difference(Close))\n```\n:::\n\n\\fontsize{14}{16}\\sf\n\n    \n\n\n\n\n## Seminar code\n\n::: {.callout appearance=\"minimal\"}\n<i class=\"bi bi-download\"></i> [week2.R](week2.R){download=\"week2.R\"}\n:::\n\n\n\n\n\n\n## Assignments\n\n* [IA1](../assignments/A1.qmd) is due on Monday 10 March.\n* [GA1](../assignments/G1.qmd) is due on Monday 24 March.\n",
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week3/tute-lo.qmd

@@ -3,16 +3,26 @@
 By the end of this tutorial, you should be able to:
 
 -   **Recognize common patterns in time series data**
-    -   Identify and describe key time series components: **trend**, **seasonal**, and **cycle**.
+
+    *   Identify and describe key time series components: **trend**, **seasonal**, and **cycle**.
+
+
 
 -   **Visualize seasonal patterns using appropriate plots**
-    -   Create **seasonal plots** using `gg_season()`.
-    -   Create and interpret **subseries plots** using `gg_subseries()`.
-    -   Understand that subseries lines—not just the horizontal average—reveal how seasonality changes over time.
+
+    *   Create **seasonal plots** using `gg_season()`.
+    *   Create and interpret **subseries plots** using `gg_subseries()`.
+    *   Understand that subseries lines—not just the horizontal average—reveal how seasonality changes over time.
+
+
 
 -   **Apply cross-sectional visualisation techniques**
-    -   Visualise and explore relationships between variables.
+
+    *   Visualise and explore relationships between variables.
+
+
 
 -   **Interpret autocorrelation and lag plots**
-    -   Understand and interpret **Autocorrelation Function (ACF)** plots.
-    -   Recognise common time series behaviour based on **ACF and lag plots**.
+
+    *   Understand and interpret **Autocorrelation Function (ACF)** plots.
+    *   Recognise common time series behaviour based on **ACF and lag plots**.