Books and journals Case studies Expert Briefings Open Access
Advanced search

Exploring the Suitability of Support Vector Regression and Radial Basis Function Approximation to Forecast Sales of Fortune 500 Companies

Vivian M. Evangelista
Rommel G. Regis

Advances in Business and Management Forecasting

ISBN: 978-1-78754-290-7, eISBN: 978-1-78754-289-1

ISSN: 1477-4070

Publication date: 6 September 2019

Abstract

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

Keywords

  • Sales forecasting
  • Time series
  • Seasonal adjustment
  • Machine learning
  • Support vector regression
  • Radial basis function

Citation

Evangelista, V.M. and Regis, R.G. (2019), "Exploring the Suitability of Support Vector Regression and Radial Basis Function Approximation to Forecast Sales of Fortune 500 Companies", Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 13), Emerald Publishing Limited, pp. 3-23. https://doi.org/10.1108/S1477-407020190000013006

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2019 Emerald Publishing Limited

Please note you do not have access to teaching notes

You may be able to access teaching notes by logging in via Shibboleth, Open Athens or with your Emerald account.
Login
If you think you should have access to this content, click the button to contact our support team.
Contact us

To read the full version of this content please select one of the options below

You may be able to access this content by logging in via Shibboleth, Open Athens or with your Emerald account.
Login
If you think you should have access to this content, click the button to contact our support team.
Contact us
Emerald Publishing
  • Opens in new window
  • Opens in new window
  • Opens in new window
  • Opens in new window
© 2021 Emerald Publishing Limited

Services

  • Authors Opens in new window
  • Editors Opens in new window
  • Librarians Opens in new window
  • Researchers Opens in new window
  • Reviewers Opens in new window

About

  • About Emerald Opens in new window
  • Working for Emerald Opens in new window
  • Contact us Opens in new window
  • Publication sitemap

Policies and information

  • Privacy notice
  • Site policies
  • Modern Slavery Act Opens in new window
  • Chair of Trustees governance statement Opens in new window
  • COVID-19 policy Opens in new window
Manage cookies

We’re listening — tell us what you think

  • Something didn’t work…

    Report bugs here

  • All feedback is valuable

    Please share your general feedback

  • Member of Emerald Engage?

    You can join in the discussion by joining the community or logging in here.
    You can also find out more about Emerald Engage.

Join us on our journey

  • Platform update page

    Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

  • Questions & More Information

    Answers to the most commonly asked questions here