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)& 代写MAT005 代写Python编程 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?& 转自：http://www.3daixie.com/contents/11/3444.html