Coursework 2021
Time Series and Forecasting
Dr Mark Tuson
Background to
the coursework
• The providers of ambulance services in
Jakarta want to understand the likely
future demand for ambulance services
in the city.
• They have provided 5 months of data
(January to May 2019).
What forecasts are they interested in?
They would like to know:
• What are the forecast number of patients for the first week in June.
• Is there any pattern in the data? – this will help them plan the allocation of resources.
What forecasts are they interested in?
• The data is available on Learning Central (20/21-MAT005 Time Series and Forecasting under the
Assessment section) within an Excel spreadsheet called TimeSeriesCourseworkData20_21.xls.
• •The Data worksheet lists the date, time of call and the city municipality from which the call
originated. The data covers the time period between 1st January 2019 and 31stMay 2019.
• •Use the data to predict the number and pattern of calls between 1st June 2019 – 7th June 2019. If
you are able to accurately predict further then please do.
• They would like this in the form of an A3 poster they can share with their colleagues.
What do you
need to do in
your analysis
• A preliminary analysis of the data including both
numerical and graphical summaries.
• Examine the components of the time series: the
underlying trend, seasonality and error and produce
a decomposition plot.
• Investigate a selection of time series models to see
which model provides a good fit to the observed
• Baseline & simple approaches, including: Naïve,
Mean, Moving Average, Simple Linear Regression.
• Complex approaches including: SES, Holt Linear, Holt
Winters, Multiple Linear Regression, ARIMAs.
• Remember to include the appropriate error statistics
and graphical comparisons for each forecasting
required within
the poster
1. An appropriate title for the poster. Please remember to
include your name and student number.
2. An introduction to the problem and how you have decided to
tackle it.
3. Numerical Summaries which describe the variation within the
4. Graphical Summaries (e.g. time plot, seasonal plot, scatter
5. Decomposition of the data to examine the trend, seasonality
and error.
6. Baseline model (e.g. Naïve)
7. Extrapolation Models (e.g. SES, Holt Linear, Holt Winters)
8. Regression (Simple Linear Regression, Multiple Linear
9. ARIMAs including an examination of autocorrelation.
10. Summary of Error Statistics for each method e.g. MSE, MAPE.
11. Summary of 7-day forecasts
12. Conclusions & recommendations
Some helpful hints
• Please remember to use an initialisation set (first70%) and a test set (remaining30%) when developing your
• Please note that the fits you experience with your models may not be perfect; you’re looking for the best
model that gives you a realistic fit to the data and will provide believable projections after the end of the
data set. As this is a real-world data set collected by busy individuals, you will certainly need to clean the
• When you are describing your preliminary analysis and the models you have used to produce your
forecasts, explain how confident you are in your forecasts and why. Discuss the difficulties you had with the
data and/or fitting the models. It makes each project individual. I am not expecting everyone to tackle this
in the same way.
• Excel
• ‘R’
• Python?
• A mixture
• I strongly recommend you use
PowerPoint, Inkscape or similar to
produce the poster (not Word)
• Excel, or text file for code
• The assignment must be handed in by
2pm on the 12th May
• Handed in through learning central
• Three documents:
• Poster in a pdf file
• Spreadsheet/CSV file containing any data
used and data analysis.
• Code in text file.
• Plagiarism will not be accepted, and if discovered will result in both students failing the coursework.
• No extensions to the deadline will be allowed.
• Don’t leave the coursework until the last minute, forecasting always takes longer than you think.
• Use it as practice for techniques that you might need during your dissertation or in a future job.
Any Questions?