Automatic covariates selection in dynamic regression models with application to COVID-19 evolution

3 pages•Published: February 16, 2023

Ana Ezquerro, Germán Aneiros Pérez and Manuel Oviedo de la Fuente

Abstract

This work introduces a new approach in time-series analysis field for automatic co- variates selection in dynamic regression models. Based on [1] and [2] previous study, a forward-selection method is proposed for adding new significant covariates from a given set. This algorithm has been implemented and optimized in R as a package, and it has been applied to multiple simulations to validate its performance. Finally, the obtained results from the IRAS database of Catalonia are presented to analyze the COVID-19 evolution.

Keyphrases: data science, dynamic regression models, time series analysis

In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, vol 14, pages 136-138.

Links:	https://easychair.org/publications/paper/1gRq
	https://doi.org/10.29007/1c23

BibTeX entry

@inproceedings{XoveTIC2022:Automatic_covariates_selection_dynamic,
  author    = {Ana Ezquerro and Germán Aneiros Pérez and Manuel Oviedo de la Fuente},
  title     = {Automatic covariates selection in dynamic regression models with application to COVID-19 evolution},
  booktitle = {Proceedings of V XoveTIC Conference. XoveTIC 2022},
  editor    = {Alvaro Leitao and Lucía Ramos},
  series    = {Kalpa Publications in Computing},
  volume    = {14},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/1gRq},
  doi       = {10.29007/1c23},
  pages     = {136-138},
  year      = {2023}}

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