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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.…
Author: | Annika Homburg, Christian H. Weiß, Layth C. Alwan, Gabriel Frahm, Rainer Göb |
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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): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |