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A Time Series Analysis Approach to Forecasting COVID-19 Cases and Deaths: An Analysis of COVID-19 Data in Colombia.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Time Series Analysis Approach to Forecasting COVID-19 Cases and Deaths: An Analysis of COVID-19 Data in Colombia./
作者:	Jackson-Sagredo, Andrea.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	76 p.
附註:	Source: Masters Abstracts International, Volume: 83-02.
Contained By:	Masters Abstracts International83-02.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28413109
ISBN:	9798516955792

A Time Series Analysis Approach to Forecasting COVID-19 Cases and Deaths: An Analysis of COVID-19 Data in Colombia.
Jackson-Sagredo, Andrea.

A Time Series Analysis Approach to Forecasting COVID-19 Cases and Deaths: An Analysis of COVID-19 Data in Colombia. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 76 p.

Source: Masters Abstracts International, Volume: 83-02.

Thesis (M.S.)--Northern Illinois University, 2021.

This item must not be sold to any third party vendors.

The novel Coronavirus, known as COVID-19 is a highly contagious and transmissible infectious disease that has taken a toll throughout the entire world for over a year. The inner workings and long term effects of COVID-19 continue to be misunderstood. While COVID-19 has impacted all countries tremendously, Latin American countries and specifically Colombia have been impacted significantly by the virus. This thesis investigates the potential to forecast COVID-19 cases and deaths using Time Series Analysis methods and models for the South American country of Colombia. Time series analysis on Colombian COVID-19 data begins with data processing on a data set consisting of COVID-19 data for all countries with reported COVID-19 case and death data. The aforementioned data set then goes through subsetting in order to obtain COVID-19 data for the country of Colombia. Model diagnostics and analysis for cases and deaths due to COVID-19 are explored using ARIMA models, and Simple Exponential Smoothing models. Models are then used to forecast cases and deaths for the next one, two, six and twelve months, respectively. Model comparison between ARIMA and SES models is performed to assess forecast accuracy for each model. Model selection measures such as AIC, BIC are utilized, while MAPE, RMSE, MAE, and MPE are utilized in comparing forecasting accuracy between the models.

ISBN: 9798516955792Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Colombia

A Time Series Analysis Approach to Forecasting COVID-19 Cases and Deaths: An Analysis of COVID-19 Data in Colombia.
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