Zusammenfassung
The aim of this paper is to present empirical results associated with forecast performance. It is known that common measures of error fail to be scale invariant, and hence cannot be used to make meaningful error comparisons on forecasts across differing time series. This offers a particular challenge toward forecast improvement when one’s intent is to compare error across different units or granularity. Moreover, although it is prudent to test many forecast methods on a time series, one cannot be sure that a single selected method will not lead to complete forecast failure. We address the aforementioned challenges by analyzing a sizable collection of time series in-house.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
J. Casals, M. Jerez, and S. Sotoca, “Modeling and forecasting time series sampled at different frequencies,” Journal of Forecasting, vol. 28, pp. 316 – 342, 07 2009.
M. Gilliland, L. Tashman, and U. Sglavo, Business Forecasting. Practical Problems and Solutions. John Wiley & Sons, Inc., 2015.
R. Hyndman, “Another look at measures of forecast accuracy,” International Journal of Forecasting, pp. 43–46, 2006.
J. S. Armstrong and F. Collopy, “Error measures for generalizing about forecasting methods: Empirical comparisons,” International Journal of Forecasting, pp. 69–80, 1992.
B. Flores, “A pragmatic view of accuracy measurement in forecasting,” Omega, pp. 93–98, 1986.
P. Wallström and A. Segerstedt, “Evaluation of forecasting error measurements and techniques for intermittent demand,” International Journal of Production Economics, pp. 625–636, 2010.
J. S. Armstrong, Principles of Forecasting: A Handbook for Researchers and Practitioners. Kluwer Academic Publishers, 2001.
R. Clemen, “Combining forecasts: A review and annotated bibliography,” International Journal of Forecasting, pp. 559–583, 1989.
P. Mertens and S. Rässler, Prognoserechnung. Physica-Verlag, 2012.
J. Croston, “Forecasting and stock control for intermittent demands,” Operational Research Quarterly, pp. 289–303, 1972.
R. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 2nd ed. Australia: OTexts, 2018.
A. Dutta and S. Shiralkar, Demand Planning with SAP APO - Concepts and Design. Espresso Tutorials GmbH, 2015.
C. Holt, “Forecasting seasonals and trends by exponential weighted moving averages,” International Journal of Forecasting, vol. 20, pp. 5–10, 03 2004.
P. Winters, “Forecasting sales by exponentially weighted moving averages,” Management Science, vol. 6, pp. 324–342, 04 1960.
J. Friedman, “Multivariate adaptive regression spline,” The Annals of Statistics, vol. 19, 03 1991.
J. Syntetos, A.A.andBoylan and J. Croston, “On the categorization of demand patterns,” Journal of the Operational Research Society, pp. 495–503, 2005.
J. S. Armstrong, “Combining forecasts: The end of the beginning or the beginning of the end?” International Journal of Forecasting, pp. 585–588, 1989.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH , ein Teil von Springer Nature
About this paper
Cite this paper
Wehinger, G., Beal, J. (2021). Forecast Aggregation and Error Comparison: An Empirical Study. In: Haber, P., Lampoltshammer, T., Mayr, M., Plankensteiner, K. (eds) Data Science – Analytics and Applications. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-32182-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-658-32182-6_5
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-32181-9
Online ISBN: 978-3-658-32182-6
eBook Packages: Computer Science and Engineering (German Language)