Observation‐driven exponential smoothing
Copyright policy )Observation‐driven exponential smoothing
This article presents an approach to forecasting count time series with a form of exponential smoothing built from observation‐driven models. The proposed method is easy to implement and simple to interpret. A variant of the approach is also proposed to handle the impact of outliers on the forecast. The performance of the methodology is studied with simulations and illustrated with an analysis of the number of monthly cases of dengue fever observed in Italy for the years 2008–2021. An R package is made available to enable the reader to reproduce the results discussed in the article.
Poisson regression, count time series, endemic-epidemic model, forecasting, Poisson regression, robustness, surveillance, Statistics, endemic-epidemic model, surveillance, forecasting, count time series, robustness
Poisson regression, count time series, endemic-epidemic model, forecasting, Poisson regression, robustness, surveillance, Statistics, endemic-epidemic model, surveillance, forecasting, count time series, robustness
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!