Graduation Semester and Year
2022
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
David Levine
Abstract
Complex time series are a ubiquitous form of data in the modern world. They have wide application across many different fields of scientific inquiry and business endeavor. Time series are used to understand and forecast weather patterns, voting patterns, computer network traffic, population health outcomes, demographic changes, the results of scientific experiments, and the performance of stocks and mutual funds. But time series can be difficult to analyze by conventional methods when the data is multivariate, incomplete, or in different formats. To address these issues, an investigation of several multivariate time series datasets was performed using the methods of automatic data discovery and derivative-based analysis. Interactive maps were constructed which displayed the results of the study. Conclusions were drawn and discussed, and an explanation was given of how this method can be applied to other multivariate time series datasets and real-world problems.
Keywords
Time series, Real estate, Stocks, Inflation, Correlation, Analysis, Algorithm
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Severynen, Peter Lawrence, "Data Discovery Analysis on Complex Time Series Data" (2022). Computer Science and Engineering Theses. 320.
https://mavmatrix.uta.edu/cse_theses/320
Comments
Degree granted by The University of Texas at Arlington