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

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Mathematical Sciences

Committee Chair

Ionut Iacob

Committee Member 1

Stephen Carden

Committee Member 2

Zheni Utic


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.

Research Data and Supplementary Material


Available for download on Friday, April 18, 2025