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MAT005 
Coursework 2020 
Time Series and Forecasting 
Dr Tracey England 
Background to the coursework 
• The manager of a local medical walk-in centre has no experience of 
time series or forecasting, and has asked for your help. 
• The aim of this coursework is to analyse the number of patients that 
attend a local walk-in centre and predict the future number of 
patients. 
• The centre manager is keen to know how many patients will come 
into the walk-in centre, each day, in the next week. 
• The centre is open 7 days a week. 
What forecasts are they interested in? 
The manager of the GP walk-in centre would like to know: 
• The number of patients that will come into the centre (each day) over the 
next 7 days. 
• Is there any pattern in the data? – this will help the manager plan the staff 
rota accordingly. 
The data 
• The data is available on Learning Central (19/20-MAT005 Time Series and Forecasting 
under the Assessment section) within an Excel spreadsheet called 
TimeSeriesCourseworkData19_20.xls. 
• The Data worksheet lists the number of patients that come in to the walk-in centre each 
day. The data covers the time period between 1st April 2015 and 31st March 2019. 
• Use the data to predict the daily number of patients between 1st April 2019 – 7th April 
2019. If you are able to accurately predict further then please do. 
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 data. 
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 model. 
Sections 
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 data. 
4. Graphical Summaries (e.g. time plot, seasonal plot, scatter 
plot) 
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 
Regression) 
9. ARIMAs including an examination of autocorrelation. 
10. Summary of Error Statistics for each method (training test 
sets, overall); e.g. MSE, MAPE 
11. Summary of 7-day forecasts 
12. Conclusions recommendations 
Some helpful hints 
• Please remember to use an initialisation set (first 70%) and a test set (remaining 30%) when 
developing your models. 
• Please note that as the data is real-world data, 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. You might need to clean the data. 
• 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. 
Computer 
Software 
Packages 
• Excel 
• ‘R’ 
• Python? 
• Other? 
• A mixture 
• Powerpoint for the poster – you will 
find it easier than using WORD. 
• Please can you keep a copy of all your 
files in case we need to see them 
Deadline 
• The assignment must be handed in to the 
Maths school office by 2pm on Thursday 
26th March 2020. A copy of these 
instructions can be found on Learning 
Central (19/20-MAT005 Time Series and 
Forecasting under the Assessment 
section). 
• You are asked to produce an A3 poster to 
describe the analysis you have carried out 
and the results you have obtained. 
• Please keep an electronic copy of your 
analysis and the poster in case we want to 
see the electronic files. 
Finally 
• 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. 
When you will 
expect to get 
feedback 
• We will aim to mark all the coursework 
by the start of the week beginning the 
20th April 2020 
• The provisional marks will be released 
during that week 
• Comments / feedback will be captured 
and can be fed back as required 
• Module marks will be fed into the 
Exam Board 
Any questions? 
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