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Performing a multivariate time series analysis on the number of undocumented immigrants apprehended and deported each year.

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Background

The inspiration behind tracking a dataset from the 2019 Yearbook of Immigration Statistics (Department of Homeland Security) was from the Netflix documentary called “Immigration Nation”. The documentary takes a deep look into recent U.S. immigration policies and its effect on undocumented immigrants and their families. The 2020 documentary highlights everything related to U.S. immigration, from the duties of an Immigration and Customs Enforcement (ICE) officer to the criminalization of undocumented immigrants.

Table of Contents

Dataset

The following tables are taken from the 2019 Yearbook of Immigration Statistics:

Preliminary Analysis

This analysis was done in August 2020 right before starting the Big Data Analytics (BDA) Program at SDSU. So I just began practicing with Python and Jupyter Notebooks. Both notebooks below are the same content.

Multivariate Time Series Analysis

This analysis was done for MIS-748 Seminar in Applied Multivariate Analytics in the Spring 2021 semester.

  • immigration.R: Most updated R script containing time series plots, ARIMA models, forecasting, intervention analysis, and impulse response plots.
  • immigration.ipynb: An IPython notebook of the R script version. However, this version is outdated. The notebook was created for easier visualization when meeting up with the professor about our preliminary analysis.
  • MIS 748 Proposal.docx: Project proposal listing the project summary and the datasets used.
  • MIS748_Final_Project_Report.pdf: The detailed report on the multivariate time series analysis of immigrants apprehended and deported.
  • Report_Latex Folder: The LaTex report source code.

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Performing a multivariate time series analysis on the number of undocumented immigrants apprehended and deported each year.

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