The forecasting competition is designed to give you real-world experience of forecasting. As part of the course we are having a real-world forecasting competition – using a model you estimate, you will be predicting time series observations. However, forecast accuracy will not affect your grade. As part of a group, you will submit your forecasts online, together with a short essay (maximum 2 pages) describing your forecasting model and resulting forecasts.

The task is to predict the number of cyclists on the Galloping Goose trail from March 26th  to April 1st (inclusive) using the “goose_competition_spring_2024.csv” file. The forecasting competition consists of two parts. Both parts have to be completed to obtain full marks:

1.   Write a short report describing your model and resulting forecasts.

2.   Submit your forecasts of the number of cyclists via Brightspace.

You may work in groups and write one report (and produce one set of forecasts) as a group, but each student must submit their own copy of the report on Brightspace. Please make sure to include the names and V-numbers of all group members on the report.

Details on both the report and submission of the actual forecasts are provided on the following two pages.

Data to be used as the basis for your forecasts will be posted on Brightspace shortly, however, you can already use the Galloping Goose data on Brightspace to estimate models using last year’s data.

1. Details on the report to be submitted:

As a group, prepare a short report on your forecasting model and resulting forecasts. This should be submitted alongside your forecasts via Brightspace. You may work in groups and write one report as a group, but each student has to submit their own copy of the report on Brightspace. Please make sure to include the names and V-numbers of all group members on the report.

The report should take the form of a short academic essay (avoid casual language, copying R-code, or showing screenshots). A good report would:

i.           Have a clear structure, such as:

a.   Introduction: introduce the forecasting problem.

b.   Data: describe the dataset, any additional variables you maybe using, any apparent patterns, trends etc. including any relevant references.

c.   Methods: describe your forecasting model, ideally including an equation that shows

your model. Carefully specify what variables are included and why you include them.

d.   Results: if applicable show your in-sample estimated model as a table (similar to the research project formatting) and plot your forecasts in a well-formatted figure,

labelling axes, clearly differentiating between in-sample observations and forecasts. For example:

Figure 1: Observed (black) and predicted (red) cyclists on the Galloping Goose.

e.    Conclusion: summarise your results, discuss how your forecasting model could be improved.

ii.         Be well-written: do not use casual language, this should be an academic piece of writing.

iii.        Be well formatted and presented: do not include screenshots of R-output, do not include   R-code. If you present regression results these should either be in the form of an equation (as we have seen in lecture slides) or as a table, and include coefficient estimates, standard errors, and the number of observations.

iv.         Be clearly referenced. Make sure to include references to any relevant datasets or literature.

v.          Not exceed 2 pages in length (excluding references).