Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
12-2023
Committee Chair or Co-Chairs
Joyner Michele Lynn
Committee Members
Knisley Jeff Randall, Zahed Mostafa
Abstract
In this study, time series decomposition techniques are used in conjunction with Kmeans clustering and Hierarchical clustering, two well-known clustering algorithms, to climate data. Their implementation and comparisons are then examined. The main objective is to identify similar climate trends and group geographical areas with similar environmental conditions. Climate data from specific places are collected and analyzed as part of the project. The time series is then split into trend, seasonality, and residual components. In order to categorize growing regions according to their climatic inclinations, the deconstructed time series are then submitted to K-means clustering and Hierarchical clustering with dynamic time warping. In order to understand how different regions’ climates compare to one another and how regions cluster based on the general trend of the temperature profile over the course of the full growing season as opposed to the seasonality component for the various locations, the created clusters are evaluated.
Document Type
Thesis - embargo
Recommended Citation
Ogedegbe, Emmanuel, "Implementation of Hierarchical and K-Means Clustering Techniques on the Trend and Seasonality Components of Temperature Profile Data" (2023). Electronic Theses and Dissertations. Paper 4270. https://dc.etsu.edu/etd/4270
Copyright
Copyright by the authors.
Included in
Applied Mathematics Commons, Computer Sciences Commons, Data Science Commons, Statistics and Probability Commons