MATH6011 FORECASTING ASSIGNMENT 2025
Your coursework must be submitted electronically via Blackboard by 3pm on Friday March 21st. Any work handed in after this time will be subject to the following penalties: 10% of your marks lost per working day up to 5 working days. Do not write your name anywhere on your work, as marking will be anonymous. Your student ID should be included in the filenames but not your name; see further instructions on file naming in Section 3 below. An extension, for bona fide reasons, may be allowed by prior agreement, but only well before the deadline; you can contact the Student Office if you would like to apply for an extension. Computer crashes or file losses a day or two before the deadline will not be an acceptable reason for an extension. It is therefore advisable to keep back-up copies of your work. Components of the project will receive different weightings in producing your final mark: 50 marks for the exponential smoothing part, 20 for ARIMA, 20 for regression, and 10 for the overall organization of your submitted material, including the description of your codes/files.
1. Background and analysis
In light of the United Nations Climate Change Conference (also known as COP29) that took place in Baku, Azerbaijan, 11–22 November 2024, the UK government through its new Global Clean Power Alliance (GCPA) initiative has employed you as a consultant. Your task is to forecast the behaviour of a number of key environmental indicators until December 2025, to help support the decision process for new policies to support the country’s efforts to reduce the impact of climate change. The data is provided by a number of public organizations, including the UK Meteorological Office and the Office for National Statistics.
1.1. How to get the data. From the four weblinks given below, download the data sets and save them in xlsx, xls, or csv format. The resulting files might have multiple columns or sheets; follow the corresponding instructions to access the data necessary for your anal- ysis. Copy the data sets from the required columns as described below; i.e., MSTA, CH4, GMAF, and ET12, scrolling down, where necessary, to find the monthly observations.
(A) Global Mean Surface Temperature Anomaly (MSTA) in 。C:
https://www.metoffice.gov.uk/hadobs/hadcrut5/data/HadCRUT.5.0.2.0/download.html
Download the monthly data in the (Global (NH+SH)/2, CSV) cell of the Summary series table under the HadCRUT.5.0.2.0 analysis section of the webpage. The MSTA data is lo- cated in the Anomaly column of the csv file.
(Source: The Meteorological Office (Met Office), which is the UK’s national weather service).
(B) Global Monthly Atmospheric Carbon Dioxide Levels (CH4):
https://climate.metoffice.cloud/greenhouse gases.html
Scroll down towards the end of the webpage (Get the data section), and download the csv file under CH4>NOAA CH4. The CH4 data to use is available in column C of the csv file.
(Source: Met Office’s Climate Dashboard).
(C) International Passenger Survey, UK visits abroad (GMAF):
https://www.ons.gov.uk/peoplepopulationandcommunity/leisureandtourism/datasets/interna tionalpassengersurveytimeseriesspreadsheet
GMAF: select the xlsx or csv file; then see the data to be used in the GMAF column (or C) of the spreadsheet, scrolling down to the monthly observations.
(Source: UK Office for National Statistics).
(D) UK Inland Monthly Energy Consumption (ET12), in million tonnes of oil equivalent. The data can be downloaded by clicking on the expression “Inland energy consumption: primary fuel input basis (ET 1.2 - monthly)” available on this webpage:
https://www.gov.uk/government/statistics/total-energy-section-1-energy-trends
ET12: use the data in the Unadjusted total column (or B) of the Month worksheet. (Source: Department for Energy Security and Net Zero).
1.2. Tasks. As it so often happens in the real world, the data sets are of different lengths.
You will have to use your own judgment in inspecting and preparing the data before carrying out any technical analysis. The analysis is in three parts:
(a) You are asked to take all four series separately and to forecast monthly behaviour until December 2025, using exponential smoothing-type forecasting methods.
(b) The GCPA team have been satisfied in the past with exponential smoothing-type fore- casting methods and are happy to see these techniques used in the analysis. However, they are interested in the possible use of the ARIMA methodology to predict MSTA. You are asked to fit the ARIMA model to MSTA, for an analysis in which you compare the use of ARIMA forecasting and a suitable exponential smoothing method. You should make a recommendation as to future use of ARIMA on this time series.
(c) The GCPA team is interested to know whether global temperatures (that is, series MSTA) are affected by carbon dioxide levels, international air travel, and the consumption of fuels (as exemplified by the time series CH4, GMAF, and ET12, respectively). Develop a multi- ple regression model, use it for the prediction of MSTA until December 2025, and report on whether you think the model is satisfactory or not.
2. What you must produce
You must produce a technical report describing all the analysis done to select the most suitable forecasting method, as well as the results obtained. The report must be accompanied by the codes used to perform the technical analysis, as well as key resulting graphs. More details on each of the aspects of the work are given in the next subsections.
2.1. The technical report. The technical report must follow the structure described in
Subsection 2.3. It should address the three parts of the analysis: exponential smoothing, ARIMA, and regression. For each part, give details of the preliminary analysis and data preparation. Also describe why each model was chosen/built and explain the analysis carried out, including an evaluation of the effectiveness of the models.
2.2. Python codes and the appendix. You must also prepare and submit Python codes
that you use to generate the results that will be included in your technical report. If any preliminary operations on your data are needed before applying/developing a Python code for your analysis, it is fine to include this in the corresponding Excel file containing your data sets. However, you must complete all the main tasks of your analysis using Python. You can use the codes from the course, different ones, or develop your own. Marking on this aspect of your work will not be based on how well you can program in Python, but rather on the functionality of your codes and their relevance in the corresponding analysis.
To help us easily know what you do in each code, you must add a single page as Appendix to your technical report, to give a brief one or two sentences description of what it does. If you do any preliminary operations on your data in the Excel file containing your data set, a line or two should also be included to describe this.
2.3. Organizing your technical report. The report must be organized as follows, with
the maximum length of 5 pages, including up to 4 pages for the core text (Sections 1 - 3) with all the relevant key illustrative graphs and up to 1 page for the Appendix. The font size and overall style must be chosen in such a way that the report is easy to read.
1. Exponential smoothing (maximal length: 2 pages; total marks: 50) Marks to be attributed based on how well you articulate the following aspects:
(a) Describe your data preparation (and its effects) prior to the implementa- tion of exponential smoothing methods.
(b) Describe the preliminary analysis undertaken (and conclusions drawn) prior to the implementation of exponential smoothing methods.
(c) Give details of how exponential smoothing models were selected for each time series, and how effective/accurate these methods are at forecasting.
(d) Quality and suitability of graphical illustrations for analysis and results. (e) Clarity and quality of presentation.
(f) Functionality of Python codes.
2. ARIMA forecasting (maximal length: 1 page; total marks: 20)
Marks to be attributed based on how well you articulate the following aspects: (a) Describe any data preparation prior to ARIMA, and its effects.
(b) Describe any preliminary analysis undertaken prior to ARIMA modelling, and the conclusions drawn.
(c) Give details of how the ARIMA model was selected, tested, and its effec- tiveness/accuracy evaluated.
(d) Compare ARIMA and exponential smoothing forecasting, both in general terms and in the particular instance of K54D.
(e) Quality and suitability of graphical illustrations for analysis and results.
(f) Clarity and quality of presentation. (g) Functionality of Python codes.
3. Regression prediction (maximal length: 1 page; total marks: 20)
Marks to be attributed based on how well you articulate the following aspects:
(a) Describe any data preparation prior to implementing the regression.
(b) Describe any preliminary analysis undertaken prior to regression modelling and the conclusions drawn.
(c) Give details of how a regression model has been selected and comment on its suitability for forecasting the variable FTSE.
(d) Quality and suitability of graphical illustrations for analysis and results. (e) Clarity and quality of presentation.
(f) Functionality of Python codes.
Appendix: Code descriptions, etc. (maximal length: 1 page; full marks: 10)
Marks here will be attributed based on the overall organization of the material that you submit, and on how clear, informative, and concise is your description of what each of your Python codes (or Excel file, in case any preliminary operations is carried out there) does.
In summary, the following guidelines must be followed while producing the technical report:
• The technical report must be organized as described above, with maximum 5 pages in total (4 pages maximum for Sections 1 - 3, and up to 1 page for the Appendix).
• No theory of forecasting is required, or repeat of the material from lectures, unless you have used models or concepts not included in the notes.
• Formal English language should be used, avoiding abbreviations (such as “doesn’t”, for example), slang, and casual vocabulary.
• In Sections 1, 2, and 3, references to codes developed/used for specific tasks can be made by using the corresponding code’s name. But no other details or description of Python modeling are needed in those sections.
• At most two sentences are needed in the Appendix to explain what each Python code (or Excel file, if necessary) does.
• Feel free to include subsections to Sections 1, 2, 3, and the Appendix, if they seem necessary to help make some parts of the report clearer.
• No introduction, table of contents, or conclusions should be written for the report.
3. Submission
All submissions should be made under the corresponding assignment tab in Blackboard.
Submit one zipped folder ( . zip), not an archived file ( . rar), without internal folders, which contains a pdf copy of the technical report, one spreadsheet with the data sets to be used for the analysis. You should also include an adequate number of files with your Python codes. Remember not to put your name anywhere on your work, as marking is anonymous. Include your student ID in your technical report and use the following naming pattern for all the files to be submitted via Blackboard (under the Assignments tab):
• One pdf file with the technical report: TechnicalReport StudentID. pdf.
• One single data file: Data StudentID . xls (with a separate sheet for each data set; namely, MSTA, CH4, GMAF, and ET12).
• Python codes: each file name should have three components, with first one related to the corresponding methodology, second to the specific task, and the third being the student’s ID. For example, if you produce/use a code to illustrate something related to the exponential smoothing, ARIMA, or regression methods, you could respectively apply the following naming patterns to your files:
– ExponentialSmoothing CH4TimePlot StudentID. py
– ARIMA ACFPlot StudentID. py
– Regression Correlation StudentID. py
The middle terms CH4TimePlot, ACFPlot, and Correlation are related to specific tasks that could be carried out under the corresponding parts. The expression used for the middle term should not exceed fifteen characters.