Publication Date

5-2021

Date of Final Oral Examination (Defense)

4-2-2021

Type of Culminating Activity

Thesis

Degree Title

Master of Science in Mathematics

Department

Mathematics

Major Advisor

Jaechoul Lee, Ph.D.

Advisor

Donna Calhoun, Ph.D.

Advisor

Hans-Peter Marshall, Ph.D.

Abstract

When fitting a model to a data set, the goal is to create a model that captures the trends present in the data. However, data often contains regions where the underlying model changes or exhibits shifts in certain parameters due to economic events. These locations in the data are known as changepoints, and ignoring them can result in high error and incorrect forecasts. By developing a specific cost function and optimizing using the genetic algorithm, we are able to locate and account for the changepoints in a given data set. We specifically apply this process to the retail sales of electricity in the United States by examining data sets from each state's residential, commercial, and industrial sectors. We demonstrate that, when changepoints are accounted for, model trends can be computed more accurately. We specifically explore this in the case of data sets that exhibit changepoints due to the 2020 (and ongoing) pandemic.

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