Recompiled

_freeze/assignments/A1/execute-results/html.json

@@ -1,8 +1,8 @@
 {
-  "hash": "5948735cd0cf24010f0d2e30714c295e",
+  "hash": "7e0f55f00aa51ba7e4218a93ee1901c5",
   "result": {
     "engine": "knitr",
-    "markdown": "---\ntitle: Assignment 1\n---\n\nThis assignment will use the same data that you will use in the [retail project](Project.qmd) later in semester. Each student will use a different time series, selected using their student ID number as follows.\n\n```r\nlibrary(fpp3)\nget_my_data <- function(student_id) {\n  set.seed(student_id)\n  all_data <- readr::read_rds(\"https://bit.ly/monashretaildata\")\n  while(TRUE) {\n    retail <- filter(all_data, `Series ID` == sample(`Series ID`, 1))\n    if(!any(is.na(fill_gaps(retail)$Turnover))) return(retail)\n  }\n}\n# Replace the argument with your student ID\nretail <- get_my_data(12345678)\n```\n\n  1. Plot your time series using the `autoplot()` command. What do you learn from the plot?\n  2. Plot your time series using the `gg_season()` command. What do you learn from the plot?\n  3. Plot your time series using the `gg_subseries()` command. What do you learn from the plot?\n  4. Find an appropriate Box-Cox transformation for your data and explain why you have chosen the particular transformation parameter $\\lambda$.\n  5. Produce a plot of an STL decomposition of the transformed data. What do you learn from the plot?\n\nFor all plots, please use appropriate axis labels and titles.\n\nYou need to submit one Quarto (`qmd`) file which implements all steps above.\n\nTo receive full marks, the `qmd` file must compile without errors.\n\n<br><br><hr><b>Due: 28 March 2025</b><br><a href=https://learning.monash.edu/mod/assign/view.php?id=3444030 class = 'badge badge-large badge-blue'><font size='+2'>&nbsp;&nbsp;<b>Submit</b>&nbsp;&nbsp;</font><br></a>\n",
+    "markdown": "---\ntitle: Assignment 1\n---\n\nThis assignment will use the same data that you will use in the [retail project](Project.qmd) later in the semester. Each student will use a different time series, selected using their student ID number as follows.\n\n```r\nlibrary(fpp3)\nget_my_data <- function(student_id) {\n  set.seed(student_id)\n  all_data <- readr::read_rds(\"https://bit.ly/monashretaildata\")\n  while(TRUE) {\n    retail <- filter(all_data, `Series ID` == sample(`Series ID`, 1))\n    if(!any(is.na(fill_gaps(retail)$Turnover))) return(retail)\n  }\n}\n# Replace the argument with your student ID\nretail <- get_my_data(12345678)\n```\n\n  1. Plot your time series using the `autoplot()` command. What do you learn from the plot?\n  2. Plot your time series using the `gg_season()` command. What do you learn from the plot?\n  3. Plot your time series using the `gg_subseries()` command. What do you learn from the plot?\n  4. Find an appropriate Box-Cox transformation for your data and explain why you have chosen the particular transformation parameter $\\lambda$.\n  5. Produce a plot of an STL decomposition of the transformed data. What do you learn from the plot?\n\nFor all plots, please use appropriate axis labels and titles.\n\nYou need to submit one Quarto (`qmd`) file which implements all steps above. You may use <a href=\"https://af.numbat.space/assignments/Assignment_template.qmd\">this file</a> as a starting point.\n\nTo receive full marks, the `qmd` file must compile without errors.\n\n<br><br><hr><b>Due: 28 March 2025</b><br><a href=https://learning.monash.edu/mod/assign/view.php?id=3444030 class = 'badge badge-large badge-blue'><font size='+2'>&nbsp;&nbsp;<b>Submit</b>&nbsp;&nbsp;</font><br></a>\n",
     "supporting": [],
     "filters": [
       "rmarkdown/pagebreak.lua"

_freeze/assignments/A2/execute-results/html.json

@@ -1,8 +1,8 @@
 {
-  "hash": "3603007ed6637ba4ae49ae900efdf387",
+  "hash": "e393193748450beef957840670f6d918",
   "result": {
     "engine": "knitr",
-    "markdown": "---\ntitle: Assignment 2\n---\n\nThis assignment will use the same data that you will use in the [retail project](Project.qmd) later in semester. Each student will use a different time series, selected using their student ID number as follows.\n\n```r\nlibrary(fpp3)\nget_my_data <- function(student_id) {\n  set.seed(student_id)\n  all_data <- readr::read_rds(\"https://bit.ly/monashretaildata\")\n  while(TRUE) {\n    retail <- filter(all_data, `Series ID` == sample(`Series ID`, 1))\n    if(!any(is.na(fill_gaps(retail)$Turnover))) return(retail)\n  }\n}\n# Replace the argument with your student ID\nretail <- get_my_data(12345678)\n```\n\n1. Using a test set of 2019--2022, fit an ETS model chosen automatically, and three benchmark methods to the training data. Which gives the best forecasts on the test set, based on RMSE?\n2. Check the residuals from the best model using an ACF plot and a Ljung-Box test. Do the residuals appear to be white noise?\n3. Now use time-series cross-validation with a minimum sample size of 15 years, a step size of 1 year, and a forecast horizon of 5 years. Calculate the RMSE of the results. Does it change the conclusion you reach based on the test set?\n4. Which of these two methods of evaluating accuracy is more reliable? Why?\n\nSubmit a Quarto (`qmd`) file which carries out the above analysis. You need to submit one file which implements all steps above.\n\nTo receive full marks, the `qmd` file must compile without errors.\n\n<br><br><hr><b>Due: 18 April 2025</b><br><a href=https://learning.monash.edu/mod/assign/view.php?id=3444031 class = 'badge badge-large badge-blue'><font size='+2'>&nbsp;&nbsp;<b>Submit</b>&nbsp;&nbsp;</font><br></a>\n",
+    "markdown": "---\ntitle: Assignment 2\n---\n\nThis assignment will use the same data that you will use in the [retail project](Project.qmd) later in the semester. Each student will use a different time series, selected using their student ID number as follows.\n\n```r\nlibrary(fpp3)\nget_my_data <- function(student_id) {\n  set.seed(student_id)\n  all_data <- readr::read_rds(\"https://bit.ly/monashretaildata\")\n  while(TRUE) {\n    retail <- filter(all_data, `Series ID` == sample(`Series ID`, 1))\n    if(!any(is.na(fill_gaps(retail)$Turnover))) return(retail)\n  }\n}\n# Replace the argument with your student ID\nretail <- get_my_data(12345678)\n```\n\n1. Using a test set of 2019--2022, fit an ETS model chosen automatically, and three benchmark methods to the training data. Which gives the best forecasts on the test set, based on RMSE?\n2. Check the residuals from the best model using an ACF plot and a Ljung-Box test. Do the residuals appear to be white noise?\n3. Now use time-series cross-validation with a minimum sample size of 15 years, a step size of 1 year, and a forecast horizon of 5 years. Calculate the RMSE of the results. Does it change the conclusion you reach based on the test set?\n4. Which of these two methods of evaluating accuracy is more reliable? Why?\n\nSubmit a Quarto (`qmd`) file which carries out the above analysis. You need to submit one file which implements all steps above. You may use <a href=\"https://af.numbat.space/assignments/Assignment_template.qmd\">this file</a> as a starting point.\n\nTo receive full marks, the `qmd` file must compile without errors.\n\n<br><br><hr><b>Due: 18 April 2025</b><br><a href=https://learning.monash.edu/mod/assign/view.php?id=3444031 class = 'badge badge-large badge-blue'><font size='+2'>&nbsp;&nbsp;<b>Submit</b>&nbsp;&nbsp;</font><br></a>\n",
     "supporting": [],
     "filters": [
       "rmarkdown/pagebreak.lua"

_freeze/assignments/A3/execute-results/html.json

@@ -1,8 +1,8 @@
 {
-  "hash": "8f55790c60d47fa5f76f432acb9da54d",
+  "hash": "a4496207094184381d65fcea02082827",
   "result": {
     "engine": "knitr",
-    "markdown": "---\ntitle: Assignment 3\n---\n\nThis assignment will use the same data that you will use in the [retail project](Project.qmd) later in semester. Each student will use a different time series, selected using their student ID number as follows.\n\n```r\nlibrary(fpp3)\nget_my_data <- function(student_id) {\n  set.seed(student_id)\n  all_data <- readr::read_rds(\"https://bit.ly/monashretaildata\")\n  while(TRUE) {\n    retail <- filter(all_data, `Series ID` == sample(`Series ID`, 1))\n    if(!any(is.na(fill_gaps(retail)$Turnover))) return(retail)\n  }\n}\n# Replace the argument with your student ID\nretail <- get_my_data(12345678)\n```\n\nUse a training set up to and including 2018.\n\n* What transformations (Box-Cox and/or differencing) would be required to make the data stationary? You should use a unit-root test as part of the discussion.\n* Use a plot of the ACF and PACF of the (possibly differenced) data to determine two plausible models for this data set.\n* Fit both models, along with an automatically chosen model, and produce forecasts for 2019--2022.\n* Which model is best based on AIC? Which model is best based on the test set RMSE? Which do you think is best to use for future forecasts? Why?\n* Check the residuals from your preferred model, using an ACF plot and a Ljung-Box test. Do the residuals appear to be white noise?\n\nSubmit a Quarto (`qmd`) file which carries out the above analysis. You need to submit one file which implements all steps above.\n\n\n<br><br><hr><b>Due: 16 May 2025</b><br><a href=https://learning.monash.edu/mod/assign/view.php?id=3444032 class = 'badge badge-large badge-blue'><font size='+2'>&nbsp;&nbsp;<b>Submit</b>&nbsp;&nbsp;</font><br></a>\n",
+    "markdown": "---\ntitle: Assignment 3\n---\n\nThis assignment will use the same data that you will use in the [retail project](Project.qmd) later in the semester. Each student will use a different time series, selected using their student ID number as follows.\n\n```r\nlibrary(fpp3)\nget_my_data <- function(student_id) {\n  set.seed(student_id)\n  all_data <- readr::read_rds(\"https://bit.ly/monashretaildata\")\n  while(TRUE) {\n    retail <- filter(all_data, `Series ID` == sample(`Series ID`, 1))\n    if(!any(is.na(fill_gaps(retail)$Turnover))) return(retail)\n  }\n}\n# Replace the argument with your student ID\nretail <- get_my_data(12345678)\n```\n\nUse a training set up to and including 2018.\n\n* What transformations (Box-Cox and/or differencing) would be required to make the data stationary? You should use a unit-root test as part of the discussion.\n* Use a plot of the ACF and PACF of the (possibly differenced) data to determine two plausible models for this data set.\n* Fit both models, along with an automatically chosen model, and produce forecasts for 2019--2022.\n* Which model is best based on AIC? Which model is best based on the test set RMSE? Which do you think is best to use for future forecasts? Why?\n* Check the residuals from your preferred model, using an ACF plot and a Ljung-Box test. Do the residuals appear to be white noise?\n\nSubmit a Quarto (`qmd`) file which carries out the above analysis. You need to submit one file which implements all steps above. You may use <a href=\"https://af.numbat.space/assignments/Assignment_template.qmd\">this file</a> as a starting point.\n\n<br><br><hr><b>Due: 16 May 2025</b><br><a href=https://learning.monash.edu/mod/assign/view.php?id=3444032 class = 'badge badge-large badge-blue'><font size='+2'>&nbsp;&nbsp;<b>Submit</b>&nbsp;&nbsp;</font><br></a>\n",
     "supporting": [],
     "filters": [
       "rmarkdown/pagebreak.lua"

_freeze/assignments/Assignment_template/execute-results/html.json

@@ -0,0 +1,17 @@
+{
+  "hash": "1b28a4143404f19211aba265ff5b2b03",
+  "result": {
+    "engine": "knitr",
+    "markdown": "---\ntitle: \"Title of your assignment\"\nauthor: Your Name\nstudent_id: 123456789 # Your student ID\nformat: html\nexecute:\n  echo: true\n---\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(fpp3)\nget_my_data <- function(student_id) {\n  set.seed(student_id)\n  all_data <- readr::read_rds(\"https://bit.ly/monashretaildata\")\n  while(TRUE) {\n    retail <- filter(all_data, `Series ID` == sample(`Series ID`, 1))\n    if(!any(is.na(fill_gaps(retail)$Turnover))) return(retail)\n  }\n}\nretail <- get_my_data(rmarkdown::metadata$student_id)\n```\n:::\n",
+    "supporting": [
+      "Assignment_template_files"
+    ],
+    "filters": [
+      "rmarkdown/pagebreak.lua"
+    ],
+    "includes": {},
+    "engineDependencies": {},
+    "preserve": {},
+    "postProcess": true
+  }
+}