Extending time series forecasting methods using functional principal components analysis

Author

Isaac Michael Wagner-Muns

Keywords and Phrases

FDA; Forecasting; FPCA; Functional Data Analysis; Functional Principal Components Analysis; Traffic

Abstract

"Traffic volume forecasts are used by many transportation analysis and management systems to better characterize and react to fluctuating traffic patterns. Most current forecasting methods do not take advantage of the underlying functional characteristics of the time series to make predictions. This paper presents a methodology that uses Functional Principal Components Analysis (FPCA) to create smooth and differentiable daily traffic forecasts. The methodology is validated with a data set of 1,813 days of 15 minute aggregated traffic volume time series. Both the FPCA based forecasts and the associated prediction intervals outperform traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) based methods"--Abstract, page iii.

Advisor(s)

Guardiola, Ivan

Committee Member(s)

Qin, Ruwen
Dagli, Cihan H., 1949-

Department(s)

Engineering Management and Systems Engineering

Degree Name

M.S. in Systems Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2017

Pagination

viii, 40 pages

Note about bibliography

Includes bibliographical references (pages 35-39).

Rights

© 2017 Isaac Michael Wagner-Muns

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 11126

Electronic OCLC #

992440670

Recommended Citation

Wagner-Muns, Isaac Michael, "Extending time series forecasting methods using functional principal components analysis" (2017). Masters Theses. 7666.
https://scholarsmine.mst.edu/masters_theses/7666