The Development and Evaluation of a Forecasting System that Incorporates ARIMA Modeling with Autoregression and Exponential Smoothing Metadata

Metadata describes a digital item, providing (if known) such information as creator, publisher, contents, size, relationship to other resources, and more. Metadata may also contain "preservation" components that help us to maintain the integrity of digital files over time.

Available Formats

Dublin Core: XML Dublin Core: JSON Dublin Core: Text Dublin Core: RDF/XML UNTL: XML Access METS: XML

Title

  • Main Title The Development and Evaluation of a Forecasting System that Incorporates ARIMA Modeling with Autoregression and Exponential Smoothing

Creator

  • Author: Simmons, Laurette Poulos
    Creator Type: Personal

Contributor

  • Chair: Kvanli, Alan
    Contributor Type: Personal
    Contributor Info: Major Professor
  • Committee Member: Conrady, Denis A.
    Contributor Type: Personal
    Contributor Info: Minor Professor
  • Committee Member: Pavur, Robert J.
    Contributor Type: Personal

Publisher

  • Name: North Texas State University
    Place of Publication: Denton, Texas

Date

  • Creation: 1985-05

Language

  • English

Description

  • Content Description: This research was designed to develop and evaluate an automated alternative to the Box-Jenkins method of forecasting. The study involved two major phases. The first phase was the formulation of an automated ARIMA method; the second was the combination of forecasts from the automated ARIMA with forecasts from two other automated methods, the Holt-Winters method and the Stepwise Autoregressive method. The development of the automated ARIMA, based on a decision criterion suggested by Akaike, borrows heavily from the work of Ang, Chuaa and Fatema. Seasonality and small data set handling were some of the modifications made to the original method to make it suitable for use with a broad range of time series. Forecasts were combined by means of both the simple average and a weighted averaging scheme. Empirical and generated data were employed to perform the forecasting evaluation. The 111 sets of empirical data came from the M-Competition. The twenty-one sets of generated data arose from ARIMA models that Box, Taio and Pack analyzed using the Box-Jenkins method. To compare the forecasting abilities of the Box-Jenkins and the automated ARIMA alone and in combination with the other two methods, two accuracy measures were used. These measures, which are free of magnitude bias, are the mean absolute percentage error (MAPE) and the median absolute percentage error (Md APE).
  • Physical Description: vi, 108 leaves: ill.

Subject

  • Keyword: forecasting
  • Keyword: automated forcasting
  • Keyword: Box-Jenkins method
  • Library of Congress Subject Headings: Forecasting -- Methodology

Collection

  • Name: UNT Theses and Dissertations
    Code: UNTETD

Institution

  • Name: UNT Libraries
    Code: UNT

Rights

  • Rights Access: public
  • Rights Holder: Simmons, Laurette Poulos
  • Rights License: copyright
  • Rights Statement: Copyright is held by the author, unless otherwise noted. All rights reserved.

Resource Type

  • Thesis or Dissertation

Format

  • Text

Identifier

  • Accession or Local Control No: 1002779379-Simmons
  • Call Number: 379 N81d no. 2347
  • UNT Catalog No.: b1885943
  • OCLC: 13029564
  • Archival Resource Key: ark:/67531/metadc332047

Degree

  • Academic Department: College of Business Administration
  • Degree Discipline: Management Science
  • Degree Level: Doctoral
  • Degree Name: Doctor of Philosophy
  • Degree Publication Type: disse
  • Degree Grantor: North Texas State University
Back to Top of Screen