Assignment 1

This assignment will use the same data that you will use in the retail project later in the semester. Each student will use a different time series, selected using their student ID number as follows.

library(fpp3)
get_my_data <- function(student_id) {
  set.seed(student_id)
  all_data <- readr::read_rds("https://bit.ly/monashretaildata")
  while(TRUE) {
    retail <- filter(all_data, `Series ID` == sample(`Series ID`, 1))
    if(!any(is.na(fill_gaps(retail)$Turnover))) return(retail)
  }
}
# Replace the argument with your student ID
retail <- get_my_data(12345678)
  1. Plot your time series using the autoplot() command. What do you learn from the plot? [1 mark]
  2. Plot your time series using the gg_season() command. What do you learn from the plot? [1 mark]
  3. Plot your time series using the gg_subseries() command. What do you learn from the plot? [1 mark]
  4. Find an appropriate Box-Cox transformation for your data and explain why you have chosen the particular transformation parameter \lambda. [1.5 marks]
  5. Produce a plot of an STL decomposition of the transformed data. What do you learn from the plot? [1.5 marks]

For all plots, please use appropriate axis labels and titles.

You need to submit one Quarto (qmd) file which implements all steps above. You may use this file as a starting point.

To receive full marks, the qmd file must compile without errors.



Due: 28 March 2025
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