The format for submitting the final project is different from the midterm project. The main reason for that is to facilitate running the peer review via Canvas.

Final project outline. Find a time series dataset of your choice. Carry out a time series analysis, taking advantage of what we have learned so far in this course. Write a report, submitted on Canvas as a zip file by the deadline, 11:59pm on Tuesday 4/19. The zip file should contain the following:

  1. A file called blinded.Rmd and its compiled version blinded.html which contain no identifying text. This version will be used for anonymous peer review and posted on the course website.

  2. Data files and any other files needed to compile the Rmd on a standard R or Rstudio environment. You can assume that the grader and peer reviewers will install required libraries as needed.

In addition to the project report, there is a separate Canvas assignment requesting submission of a short individual statement describing (i) your role in the group; (ii) how the group shared the tasks involved in writing the report. This is not necessary if you carried out an individual final project. The individual roles statement can be as short as a paragraph. Points for this individual report will be awarded in the teamwork category of the grading rubric below. It is expected that every group member will be awarded the same score. However, in special circumstances, these individual statements could be used to make appropriate adjustments.

Choice of data and the analysis task. It is expected that part of your project will involve a POMP analysis, using the modeling and inference approaches we have studied in the second half of this semester. A common goal of POMP analysis is to connect theory to data. To do this, you must think about both the theory and the data. If possible, choose a dataset on a topic for which you know, or are willing to discover, some background theory. A good way to get ideas for topics to study and places to find data is to look at the past final projects from 2021 or previous years (2016, 2018, 2020). Each of these projects should contain the data and information about where the data came from. You may want to search for your own data set, but it is also legitimate to re-analyze data from a previous final project. If you do re-analyze data, you should explain clearly how your analysis goes beyond the previous work, and you should be especially careful to give proper credit to any code you reuse. Also, note that old pomp code may need modification to run on the current version of pomp. The pomp version 2 upgrade guide can be helpful for older code. The changes from pomp2.0 to the current pomp4.1 are smaller.

As for the midterm project, the time series should hopefully have at least 100 time points. You can have less, if your interests demand it. Shorter data needs additional care, since model diagnostics and asymptotic approximations become more delicate on small datasets. If your data are longer than, say, 1000 time points, you can subsample if you start having problems working with too much data. Computational considerations may prevent you analyzing as large a model, or as long a dataset, as you would ideally do. That is fine. You can present a smaller, computationally feasible analysis and discuss possible extensions to your analysis.


Groups. There will be randomly assigned groups of approximately 3 people. As pointed out in the syllabus, in special situations you can request to write an individual midterm project. This may be appropriate if you have a particular dataset or scientific question that has motivated your interest in learning time series analysis. You must request this before groups are assigned. Once you are in a group you have to come to an agreement with your group on what data to analyze. You will not be able to request your own group partners - in order to treat everyone fairly, groups will be randomized.


Data privacy and project anonymity. The projects, together with their data and source code, will be posted anonymously on the class website unless you have particular reasons why this should not be done. For example, you may have access to data with privacy concerns. The projects will be posted anonymously. After the semester is finished, you can request for your name(s) to be added to your project if you want to.


More comments on choice of data and data analysis goals.


Methodology not covered in class. This course has focused on ARMA and POMP models, two related approaches to time domain analysis of time series. For example, we have not spent much time on frequency domain analysis of multivariate time series. If you decide that alternative approaches are particularly relevant for your data analysis goal, you can use them in your project as a complementary approach to what we have covered in class. Eplaining and justifying an alternative approach can be a substantial component of the project.


Expectations for the report. The final report will be graded on the following categories, the same as for the midterm project.


Plagiarism. All sources are allowed. You can access any website and talk to any human about your project, as long as the interaction is properly credited. If material is taken directly from another source, that source must be cited and the copied material clearly attributed to the source, for example by the use of quotation marks. Failing to do this is plagiarism and will, at a minimum, result in zero credit for the scholarship category. For course projects, we should be at least as careful with attributions than the high standards expected across academia. Explaining how you gathered information for your project can strengthen the presentation. For example, any time you discuss your project with a classmate, or you use advice from Stack Overflow, you can add this to the acknowledgements section of your project.

Further discussion of plagiarism can be found in On Being a Scientist: A Guide to Responsible Conduct in Research: Third edition (2009), by The National Academies Press. Here is how the Rackham Academic and Professional Integrity Policy describes plagiarism:

8.1.2 Plagiarism

Includes:

Representing the words, ideas, or work of others as one’s own in writing or presentations, and failing to give full and proper credit to the original source.

Failing to properly acknowledge and cite language from another source, including paraphrased text.

Failing to properly cite any ideas, images, technical work, creative content, or other material taken from published or unpublished sources in any medium, including online material or oral presentations, and including the author’s own previous work.