Assignment 2

This assignment will use the same data that you will use in the retail project later in 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?
  2. Plot your time series using the gg_season() command. What do you learn from the plot?
  3. Plot your time series using the gg_subseries() command. What do you learn from the plot?
  4. Find an appropriate Box-Cox transformation for your data and explain why you have chosen the particular transformation parameter \lambda.
  5. Produce a plot of an STL decomposition of the transformed data. What do you learn from the plot?

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

You need to submit one Rmarkdown or Quarto file which implements all steps above.

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



Due: 24 March 2024
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