Coherent forecasting of NoGeAR(1) model
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917787818000384 |
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| author | Andrews, Divya Kuttenchalil Balakrishna, N. |
| author_facet | Andrews, Divya Kuttenchalil Balakrishna, N. |
| contents | This article focuses on the coherent forecasting of the recently introduced novel geometric AR(1) (NoGeAR(1)) model - an INAR model based on inflated - parameter binomial thinning approach. Various techniques are available to achieve h - step ahead coherent forecasts of count time series, like median and mode forecasting. However, there needs to be more body of literature addressing coherent forecasting in the context of overdispersed count time series. Here, we study the forecasting distribution corresponding to NoGeAR(1) process using the Monte Carlo (MC) approximation method. Accordingly, several forecasting measures are employed in the simulation study to facilitate a thorough comparison of the forecasting capability of NoGeAR(1) with other models. The methodology is also demonstrated using real-life data, specifically the data on CWß TeXpert downloads and Barbados COVID-19 data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00304 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Coherent forecasting of NoGeAR(1) model Andrews, Divya Kuttenchalil Balakrishna, N. Methodology Statistics Theory 62M10 This article focuses on the coherent forecasting of the recently introduced novel geometric AR(1) (NoGeAR(1)) model - an INAR model based on inflated - parameter binomial thinning approach. Various techniques are available to achieve h - step ahead coherent forecasts of count time series, like median and mode forecasting. However, there needs to be more body of literature addressing coherent forecasting in the context of overdispersed count time series. Here, we study the forecasting distribution corresponding to NoGeAR(1) process using the Monte Carlo (MC) approximation method. Accordingly, several forecasting measures are employed in the simulation study to facilitate a thorough comparison of the forecasting capability of NoGeAR(1) with other models. The methodology is also demonstrated using real-life data, specifically the data on CWß TeXpert downloads and Barbados COVID-19 data. |
| title | Coherent forecasting of NoGeAR(1) model |
| topic | Methodology Statistics Theory 62M10 |
| url | https://arxiv.org/abs/2403.00304 |