An R package that unifies spatio-temporal STARMA and GARCH-type models through double generalized linear models, allowing simultaneous modeling of means and dispersion for count or continuous spatial time series.
tscount: An R package for analysis of count time series following Generalized Linear Models.Journal of Statistical Software, 82:1–51
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Mean scaled score is recommended over rank-based aggregation for model selection on time series datasets because skewness causes non-mean criteria to select misspecified models with short tests.
Standard count time series models with pandemic break indicators applied to US and Italian transplant data capture COVID deviations, show deceased-donor recovery to baselines, and find auxiliary COVID covariates add negligible predictive value beyond autoregressive and calendar terms.
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glmSTARMA -- An R-Package for fitting autoregressive spatio-temporal models following generalized linear models
An R package that unifies spatio-temporal STARMA and GARCH-type models through double generalized linear models, allowing simultaneous modeling of means and dispersion for count or continuous spatial time series.
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Model selection with proper scoring rules on data sets of time series: prefer the mean scaled score
Mean scaled score is recommended over rank-based aggregation for model selection on time series datasets because skewness causes non-mean criteria to select misspecified models with short tests.
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Scalable model selection for count time series with structural breaks: application to solid-organ transplantation during and after COVID-19 in the USA and Italy
Standard count time series models with pandemic break indicators applied to US and Italian transplant data capture COVID deviations, show deceased-donor recovery to baselines, and find auxiliary COVID covariates add negligible predictive value beyond autoregressive and calendar terms.