Abstract
This paper presents the use of the Poisson probability distribution for forecasting discrete time series. The properties of the weights using the Poisson distribution are discussed. These weights provide alternatives, not attainable by exponential smoothing models. As an introduction to Poisson smoothing process, a constant and linear trend correction models are presented. For some of the time series tests, the Poisson forecasting models show slightly improved forecast accuracies compared to exponential forecasting models.
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© 2015 Academy of Marketing Science
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Nathan, J. (2015). Poisson Forecasting Models: An Introduction. In: Bellur, V. (eds) The 1980’s: A Decade of Marketing Challenges. Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Springer, Cham. https://doi.org/10.1007/978-3-319-16976-7_72
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DOI: https://doi.org/10.1007/978-3-319-16976-7_72
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16975-0
Online ISBN: 978-3-319-16976-7
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