When you are describing your preliminary analysis and the models you have used to produce your forecasts

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

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

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 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 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 models.

• 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 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?

• A mixture

• I strongly recommend you use PowerPoint, Inkscape or similar to produce the poster (not Word)

• Excel, or text file for code

Deadline 

• 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.

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.

Any Questions?

Attachments:

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