Term of Award


Degree Name

Master of Science in Mathematics (M.S.)

Document Type and Release Option

Thesis (open access)

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

Patricia Humphrey

Committee Member 1

J. Duggins

Committee Member 2

B. Oluyede


A time series is a sequence of data points, typically measured at uniform time intervals. Examples occur in a variety of fields ranging from economics to engineering, and methods of analyzing time series constitute an important part of Statistics. Time series analysis comprises methods for analyzing time series data in order to extract meaningful characteristics of the data and forecast future values. The Autoregressive Integrated Moving Average (ARIMA) models, or Box-Jenkins methodology, are a class of linear models that are capable of representing stationary as well as nonstationary time series. ARIMA models rely heavily on autocorrelation patterns. This paper will explore the application of the Box-Jenkins approach to stock prices, in particular sampling at different time intervals in order to determine if there is some optimal frame and if there are similarities in autocorrelation patterns of stocks within the same industry.

Research Data and Supplementary Material