PMF forecasting for count processes
A comprehensive performance analysis
Publication date
2023-04-04
Document type
Konferenzbeitrag
Author
Conference
7th International Conference on Time Series and Forecasting, ITISE 2021 ; Gran Canaria, Spain ; July 19-21, 2021
Publisher
Springer
Book title
Theory and applications of time series analysis and forecasting : selected contributions from ITISE 2021
First page
79
Last page
90
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Coherent forecasting
Count time series
Estimation error
Forecast distribution
Mean squared error
Abstract
Coherent forecasting techniques account for the discrete nature of count processes. Besides point and interval forecasts, a third way for achieving coherent forecasts is to consider the full predictive probability mass function (PMF) as the actual forecast value. For a large variety of count processes, the performance of PMF forecasting under estimation uncertainty is analyzed. Furthermore, also Gaussian approximate PMF forecasting is investigated. Different approaches for performance evaluation are taken into consideration, with the main focus on mean squared errors computed for either the full PMF or its lower and upper tails, respectively. A real-world example from finance is presented for illustration.
Version
Published version
Access right on openHSU
Metadata only access