Individual Assignment 1 (5%)

You must provide forecasts for the following items:

  1. Google closing stock price on 21 March 2025 [Data].
  2. Maximum temperature at Melbourne airport on 11 April 2025 [Data].
  3. The difference in points (Collingwood minus Essendon) scored in the AFL match between Collingwood and Essendon for the Anzac Day clash. 25 April 2025 [Data].
  4. The seasonally adjusted estimate of total employment for April 2025 in (’000). ABS CAT 6202, to be released around mid May 2025 [Data].
  5. Google closing stock price on 23 May 2025 [Data].

For each of these, give a point forecast and an 80% prediction interval, and explain in a couple of sentences how each was obtained.

Submission

Your forecasts should be submitted in moodle by 9.30am, on Monday, March 10.



Due: 10 March 2025
  Submit (ETF5231)  


Due: 10 March 2025
  Submit (ETF3231)  

Tips

  • The [Data] links give you possible data to start with, but you are free to use any data you like.
  • There is no need to use any fancy models or sophisticated methods. Simple is better for this assignment. The methods you use should be understandable to any high school student.

Forecasting competition

Once the true values in each case are available, we will come back to this exercise and see who did the best using the scoring method described in class. The student with the best score will be the winner of our forecasting competition (something really nice to have on your CV), and will also win a $100 Amazon gift voucher.

Note: your assignment mark is not dependent at all on the score you obtain in the competition.

Scoring

Let y= actual, \hat{y}= point forecast, [\hat{\ell},\hat{u}]= prediction interval

Point forecasts:

\text{Absolute Error} = |y-\hat{y}|

  • Rank results for all students in class
  • Add ranks across all five items

Prediction intervals:

\text{Interval Score} = (\hat u - \hat\ell) + 10(\hat\ell - y)_+ + 10 (y-\hat u)_+

  • u_+=max(u,0)
  • Rank results for all students
  • Add ranks across all five items