Term of Award
Spring 2024
Degree Name
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (restricted to Georgia Southern)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mathematical Sciences
Committee Chair
Ionut Iacob
Committee Member 1
Stephen Carden
Committee Member 2
Zheni Utic
Abstract
Time series are numerical sequences typically produced by collecting process data at regular time intervals. They appear in many applications and their analysis has been a continuous research topic over many decades. Artificial Neural Networks are sophisticated mathematical models inspired by how human brain neurons are interconnected to produce decisions and judgements. They are parameter models that can be adapted to successfully discover trends and patterns in various types of static or dynamic data (sequential, images, sound, etc.), and produce predictions of further behavior of dynamic data.
This work adapts the power of the Artificial Neural Networks and proposes a novel hierarchical model for finding trends and cyclic patterns in time series data. Our model can be used to perform practical analysis of past time series data (financial, sales, or biological data) and can provide useful insights into the near future evolution of such data.
Recommended Citation
Farotimi, Oluwasayo, "Time Series Analysis Using Hierarchical Neural Network-Based Models" (2024). Electronic Theses and Dissertations. 2744.
https://digitalcommons.georgiasouthern.edu/etd/2744
Research Data and Supplementary Material
No
