A performance analysis of prediction intervals for count time series

Artikel

One of the major motivations for the analysis and modeling of time series data is the forecasting of future outcomes. The use of interval forecasts instead of point forecasts allows us to incorporate the apparent forecast uncertainty. When forecasting count time series, one also has to account for the discreteness of the range, which is done by using coherent prediction intervals (PIs) relying on a count model. We provide a comprehensive performance analysis of coherent PIs for diverse types of count processes. We also compare them to approximate PIs that are computed based on a Gaussian approximation. Our analyses rely on an extensive simulation study. It turns out that the Gaussian approximations do considerably worse than the coherent PIs. Furthermore, special characteristics such as overdispersion, zero inflation, or trend clearly affect the PIs' performance. We conclude by presenting two empirical applications of PIs for count time series: the demand for blood bags in a hospital and the number of company liquidations in Germany.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Journal of Forecasting ; ISSN: 1099-131X ; Volume: 40 ; Year: 2021 ; Issue: 4 ; Pages: 603-625 ; Hoboken, NJ: Wiley

Klassifikation und Themen

• Thema: coherent forecasting ; count time series ; estimation error ; Gaussian approximation ; prediction interval

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Homburg, Annika ; Weiß, Christian H. ; Alwan, Layth C. ; Frahm, Gabriel ; Göb, Rainer
• Ereignis: Veröffentlichung
• (wer): Wiley
• (wo): Hoboken, NJ
• (wann): 2021

Weitere Informationen

• DOI: doi:10.1002/for.2729
• Handle: http://hdl.handle.net/10419/230179
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ