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?