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IMPROVING LONG RANGE FORECAST ERRORS FOR BETTER CAPACITY DECISION MAKING

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Date Issued:
2013
Abstract/Description:
Long-range demand planning and capacity management play an important role for policy makers and airline managers alike. Each makes decisions regarding allocating appropriate levels of funds to align capacity with forecasted demand. Decisions today can have long lasting effects. Reducing forecast errors for long-range range demand forecasting will improve resource allocation decision making. This research paper will focus on improving long-range demand planning and forecasting errors of passenger traffic in the U.S. domestic airline industry. This paper will look to build upon current forecasting models being used for U.S. domestic airline passenger traffic with the aim of improving forecast errors published by Federal Aviation Administration (FAA). Using historical data, this study will retroactively forecast U.S. domestic passenger traffic and then compare it to actual passenger traffic, then comparing forecast errors. Forecasting methods will be tested extensively in order to identify new trends and causal factors that will enhance forecast accuracy thus increasing the likelihood of better capacity management and funding decisions.
Title: IMPROVING LONG RANGE FORECAST ERRORS FOR BETTER CAPACITY DECISION MAKING.
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Name(s): Nizam, Anisulrahman, Author
Leon, Steven, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2013
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Long-range demand planning and capacity management play an important role for policy makers and airline managers alike. Each makes decisions regarding allocating appropriate levels of funds to align capacity with forecasted demand. Decisions today can have long lasting effects. Reducing forecast errors for long-range range demand forecasting will improve resource allocation decision making. This research paper will focus on improving long-range demand planning and forecasting errors of passenger traffic in the U.S. domestic airline industry. This paper will look to build upon current forecasting models being used for U.S. domestic airline passenger traffic with the aim of improving forecast errors published by Federal Aviation Administration (FAA). Using historical data, this study will retroactively forecast U.S. domestic passenger traffic and then compare it to actual passenger traffic, then comparing forecast errors. Forecasting methods will be tested extensively in order to identify new trends and causal factors that will enhance forecast accuracy thus increasing the likelihood of better capacity management and funding decisions.
Identifier: CFH0004425 (IID), ucf:45115 (fedora)
Note(s): 2013-05-01
B.S.B.A.
Business Administration, Dept. of Finance
Bachelors
This record was generated from author submitted information.
Subject(s): forecasting
airline travel demand
forecast accuracy
forecast error
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFH0004425
Restrictions on Access: campus 2016-04-01
Host Institution: UCF

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