Dataset Information

Count Data Time Series Modelling in Julia-The CountTimeSeries.jl Package and Applications.

ABSTRACT: A new software package for the Julia language, CountTimeSeries.jl, is under review, which provides likelihood based methods for integer-valued time series. The package's functionalities are showcased in a simulation study on finite sample properties of Maximum Likelihood (ML) estimation and three real-life data applications. First, the number of newly infected COVID-19 patients is predicted. Then, previous findings on the need for overdispersion and zero inflation are reviewed in an application on animal submissions in New Zealand. Further, information criteria are used for model selection to investigate patterns in corporate insolvencies in Rhineland-Palatinate. Theoretical background and implementation details are described, and complete code for all applications is provided online. The CountTimeSeries package is available at the general Julia package registry.

SUBMITTER: Stapper M

PROVIDER: S-EPMC8228825 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Publications

Count Data Time Series Modelling in Julia-The CountTimeSeries.jl Package and Applications.

Stapper Manuel M

Entropy (Basel, Switzerland) 20210525 6

A new software package for the Julia language, CountTimeSeries.jl, is under review, which provides likelihood based methods for integer-valued time series. The package's functionalities are showcased in a simulation study on finite sample properties of Maximum Likelihood (ML) estimation and three real-life data applications. First, the number of newly infected COVID-19 patients is predicted. Then, previous findings on the need for overdispersion and zero inflation are reviewed in an application ...[more]

PMID: 34070616

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Dataset Information

Count Data Time Series Modelling in Julia-The CountTimeSeries.jl Package and Applications.

Publications

Count Data Time Series Modelling in Julia-The CountTimeSeries.jl Package and Applications.

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