MS (Master of Science)
Date of Award
Committee Chair or Co-Chairs
Joyner Michele Lynn
Knisley Jeff Randall, Zahed Mostafa
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.
Thesis - embargo
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 by the authors.
Available for download on Sunday, September 15, 2024