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MAT005 Coursework
创建日期：2024-04-10 15:08:44
所属分类：数学mathematics
标签： MAT005
MAT005 Coursework

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MAT005
Coursework 2021

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
(January to May 2019).
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?


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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 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