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MSCI 570 Data Exploration of real-world time series
创建日期：2024-04-20 15:45:17
标签： MSCI 570
Data Exploration of real-world time series

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Coursework Information & Submission


This is the first of two assignments. This first (group) assignment is weighted 40% and will require you to explore three
time series assigned to your group. Completing this assignment will prepare you for the second assignment, weighted
60%, which will be an individual assignment and will require you to forecast time series using a variety of forecasting
algorithms and models.


Standard departmental penalties will apply for late work unless you have been given an extension for exceptional
reasons from the course administrator. All submissions will be checked by the plagiarism software. Coursework must
be submitted online on Moodle. Submit your report PLUS all R scripts in the appendix in Moodle.


Assignment: Data Exploration of real-world time series

Your task is to explore and critically discuss the time series pattern(s) of three real-world time series. Document your
findings comprehensively in a technical report, making adequate use of (readable and correctly labelled) graphs which
you also critically discuss to support your arguments. Base your justification on evidence and document your iterative
data exploration process, possibly transforming the time series, and analysing the resulting patterns throughout.


40 % of points – Explore graphically & verbally.

Explore the regular components and the irregular components of the time series making good use of graphs, plots,
and descriptive summary statistics. Critically discuss the patterns you observe verbally, both regular (level, trend,
season, etc.) and irregular patterns (outliers, level shifts, etc.) and conclude what patterns are observed. Consider
transforming the time series to exemplify individual patterns, both by removing patterns or transforming the time series
into an aggregate form of lower frequency (e.g. quarterly, monthly, weekly buckets instead of daily).

20% of points – Statistical Tests

Explore the data using statistical tests. Complement your analysis with statistical tests to support your visual analysis,
interpret their outputs, and discuss potential discrepancies with your visual analysis. Consider using alternative
packages in R to find suitable tests. Note that important tests relate not only to stationarity but also to regular time
series patterns (e.g. seasonality, trend, etc) and irregular time series patterns (outliers, structural breaks)

20% of points – ACF Analysis

Explore the data using ACF & PACF. Plot the ACF and / or PACF graphs of the time series as suitable. Also consider
to iteratively transform the time series depending on the patterns to consider non-stationarity, trends and / or
seasonality and demonstrate the effect of transformations on ACF/PACF graphs with correct interpretation.

10% of points – Conclusions

Conclude by recommending one (or multiple) suitable algorithm(s) and forecasting model form(s), and critically
discuss your choice weighting the different options. As time series patterns are not always clear, there often are
multiple suitable forecasting model for each time series. Please recommend all that are suitable.

10% of points - General report writing skills

General report writing skills include a critical discussion of findings, thoroughness of documentation, clarity of
arguments, structure of the report, readability of the report (i.e. lack of spelling and grammatical mistakes etc.) in
marking each section. Please see next page for some more technical considerations on report writing.

SUM 100%

We highly recommend using R, but you are free to use any external software but report the software used.
Please also consider the general recommendations on writing a technical report on the next page!

General suggestions on writing a report

The coursework requires you to document your analysis and critically discuss your chosen experimental design,
modelling approaches and the results in a technical report. This technical report should be written as if tailored to an
Analytics specialist (e.g. who has an MSc from Lancaster University and has taken the MSCI750 course, and who
wants to evaluate your results AND your decision making process to determine your skills in modelling and whether
you have missed anything). This means that you are not required to write a general description (i.e. a statistical test is,
the ACF function is, Exponential Smoothing is ... ) as an Analytics expert would be aware of this! Consequently, the
report should document the process of modelling, and allow an understanding of your choices and a replication of
your experiments.
The report should contain an introduction and a summary with conclusions on your findings, numbered headings, list
of figures and tables and an executive summary (tailored to senior management) indicating the most relevant findings.
The report should display a logical and concise structure, be generally “readable” and support your argument using
plots of time series, forecasts and /or accuracy. Make adequate use of graphs to show time series, model fit /
predictions and residuals to support your arguments (for this, graphs must be completely readable and with labels), as
well as tables to compare results.
The page limit for the report is 20 pages (note this is a maximum to make your life easier - you can produce shorter
reports! Pages count only for main text incl. graphs and tables, but not for the cover sheet, executive summary,
contents sheet or appendices). Reports of excessive length will be penalised by deducting 10 marks (i.e. 10% of 100)
but only if they are including un-necessary material. For formatting, use single spacing, format normal text in times
new roman font size 12, text in tables, figure and table headings in font size 10, and leave 2cm of margin left and right.
Excessive evidence (e.g. the complete information from statistical tests) may be placed in the appendix, but must be
referenced directly at the corresponding place in the main the text, else it Is not taken into consideration. Include any
technical details and hardcopies that support your arguments in a set of appendices (i.e. the printouts from ADF tests
in the appendix, with only the conclusion of significance / insignificance at a probability in the main text), which will not
count towards the page limit. You must ensure the main text is readable and that your argument is coherent without
needing to consult the appendices. All parts of the text supported by an appendix must cross-reference directly to the
relevant part.
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