# 代写MAT005 代写Python编程

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
• 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
• 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
• Please remember to use an initialisation set (first 70%) and a test set (remaining 30%) when
• 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
• 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?