A performance analysis of prediction intervals for count time series
Publication date
2020-09-19
Document type
Forschungsartikel
Author
Scopus ID
Publisher
Wiley
Series or journal
Journal of Forecasting
ISSN
Periodical volume
40
Periodical issue
4
First page
603
Last page
625
Part of the university bibliography
✅
Language
English
Keyword
coherent forecasting
count time series
estimation error
Gaussian approximation
prediction interval
Abstract
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.
Description
This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Version
Published version
Access right on openHSU
Metadata only access
Open Access Funding
Wiley (DEAL)