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Una metodología de clustering para agrupar series temporales en regiones contiguas

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  • Pablo Quintana

Abstract

Los algoritmos de clustering con restricción espacial existentes están enfocados principalmente en obtener regiones contiguas, pero no hay muchos trabajos que contemplen la agrupación de series temporales. En este paper se propone una nueva metodología de regionalización que contempla los avances que se vienen haciendo en el campo de redes neuronales y aprendizaje no supervisado denominada spatial constraints into deep embedding clustering (SDEC). El propósito del algoritmo es agrupar unidades espaciales transformando el espacio de atributos en otro de menor dimensión representado por variables latentes que logren destacar la característica del grupo al que pertenecen. Se realizaron simulaciones de series temporales por cada unidad espacial en dónde, SDEC muestra resultados superiores con respecto a otros métodos existentes en la literatura. Con el objetivo de aplicar el método a problemas de la vida real, se hace un breve estudio de la evolución del virus del Covid 19 por departamento o partido de la República Argentina, posteriormente se trata de llegar a una interpretación de los resultados obtenidos para evaluar el funcionamiento del algoritmo.

Suggested Citation

  • Pablo Quintana, 2022. "Una metodología de clustering para agrupar series temporales en regiones contiguas," Asociación Argentina de Economía Política: Working Papers 4589, Asociación Argentina de Economía Política.
    • Handle: RePEc:aep:anales:4589
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    References listed on IDEAS

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    1. Billings, Stephen B. & Johnson, Erik B., 2012. "The location quotient as an estimator of industrial concentration," Regional Science and Urban Economics, Elsevier, vol. 42(4), pages 642-647.
    2. Robert Haining & Stephen Wise & Jingsheng Ma, 2000. "Designing and implementing software for spatial statistical analysis in a GIS environment," Journal of Geographical Systems, Springer, vol. 2(3), pages 257-286, September.
    3. Steve Wise & Robert Haining & Jingsheng Ma, 1997. "Regionalisation Tools for the Exploratory Spatial Analysis of Health Data," Advances in Spatial Science, in: Manfred M. Fischer & Arthur Getis (ed.), Recent Developments in Spatial Analysis, chapter 5, pages 83-100, Springer.
    4. Marie Chavent & Vanessa Kuentz-Simonet & Amaury Labenne & Jérôme Saracco, 2018. "ClustGeo: an R package for hierarchical clustering with spatial constraints," Computational Statistics, Springer, vol. 33(4), pages 1799-1822, December.
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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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