A performance analysis of prediction intervals for count time series

Please always quote using this URN: urn:nbn:de:bvb:20-opus-217906
  • 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 toOne 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.show moreshow less

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Metadaten
Author: Annika Homburg, Christian H. Weiß, Layth C. Alwan, Gabriel Frahm, Rainer Göb
URN:urn:nbn:de:bvb:20-opus-217906
Document Type:Journal article
Faculties:Fakultät für Mathematik und Informatik / Institut für Mathematik
Language:English
Parent Title (English):Journal of Forecasting
Year of Completion:2021
Volume:40
Issue:4
First Page:603
Last Page:609
Source:Journal of Forecasting 2021, 40(4):603-625. DOI: 10.1002/for.2729
DOI:https://doi.org/10.1002/for.2729
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Tag:Gaussian approximation; coherent forecasting; count time series; estimation error; prediction interval
Release Date:2021/08/18
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International