Homework 3

Time Series Graphics Part 2

Getting started

Start with the following steps:

  • Go to our RStudio Server at http://turing.cornellcollege.edu:8787/

  • Open the respective file from the shared STA364_inst_files folder mentioned above. It will be named something like hw-03_LAST_NAME.qmd.

  • Then you need to save your copy. Click File -> Save as -> Navigate to the folder STA364_Projects (that we share) -> Change the “LAST_NAME part of the file name to your last name -> Save.

  • Update the top of the document, called the YAML, with your name.

Homework Instructions

Note

Be sure to include the relevant R code and complete sentences answering each question (e.g., if I ask for the average, you can output the answer in R but also write a complete sentence with the answer). Be sure to save your files frequently!

From this point forward, you need to start commenting on your graphs. What do you observe? Are there trends/cycles/seasonal effects? Outliers? Other interesting features?

Data for the homework is in the STA364_inst_files > data folder.

Exercises

Question 1 (fpp7)

  1. Monthly Australian retail data is provided in aus_retail. Select one of the time series as follows (but choose a different seed value):
set.seed(12345678)
myseries <- aus_retail |>
  filter(`Series ID` == sample(aus_retail$`Series ID`,1))

Explore your chosen retail time series using the following functions: autoplot, gg_season(), gg_subseries(), gg_lag(), ACF()|>autoplot().

Question 2 (fpp8)

  1. Use the following graphics functions: autoplot(), gg_season(), gg_subseries(), gg_lag(), ACF() and explore features from the following time series: “Total Private” Employed from us_employment, Bricks from aus_production, Hare from pelt, “H02” Cost from PBS, and Barrels from us_gasoline.

  1. Can you spot any seasonality, cyclicity, or trend?

  2. What do you learn about the series?

  3. What can you say about the seasonal patterns?

  4. Can you identify any unusual years?

Question 3 (fpp10)

  1. The aus_livestock data contains the monthly total number of pigs slaughtered in Victoria, Australia, from Jul 1972 to Dec 2018. Use filter() to extract pig slaughters in Victoria between 1990 and 1995. Use autoplot() and ACF() for this data. How do they differ from white noise? If a more extended period of data is used, what difference does it make to the ACF?

Question 4 (fpp11)

    1. Use the following code to compute the daily changes in Google closing stock prices. Explain what lines 2 through 5 are doing.
dgoog <- gafa_stock |>                                    # line 1
  filter(Symbol == "GOOG", year(Date) >= 2018) |>         # line 2
  mutate(trading_day = row_number()) |>                   # line 3
  update_tsibble(index = trading_day, regular = TRUE) |>  # line 4
  mutate(diff = difference(Close))                        # line 5

  1. Why was it necessary to re-index the tsibble?

  2. Plot these differences and their ACF.

  3. Do the changes in the stock prices look like white noise?

Submission

When you are finished with your homework, be sure to Render the final document. Once rendered, you can download your file by:

  • Finding the .html file in your File pane of RStudio (on the bottom right of the screen)
  • Click the check box next to the file
  • Click the blue gear above and then click “Export” to download
  • Submit your final HTML document to the respective assignment on Moodle