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Estudio comparativo de modelos clásicos de series temporales y métodos de machine learning para la predicción de la temperatura diaria de Gijón.

Authors: Álvarez Fernández, Rodrigo;

Estudio comparativo de modelos clásicos de series temporales y métodos de machine learning para la predicción de la temperatura diaria de Gijón.

Abstract

Las series temporales son secuencias de datos organizados en orden cronológico y están muy presentes en nuestro día a día. Este trabajo se centra en el análisis de series temporales con un enfoque específico en el estudio de la temperatura media diaria en Gijón. Se presentan los conceptos fundamentales de las series temporales y se abordan los pasos de un preprocesamiento de una base de datos, incluyendo estrategias para tratar con datos faltantes. Además, se introducen y aplican varios modelos y métodos para el análisis y predicción de este tipo de datos, como los modelos de suavizado exponencial y SARIMA y los métodos de Seasonal Naive, KNN y SVM. Mediante el uso de estos modelos y métodos, se busca comprender y predecir los patrones y tendencias de la temperatura en Gijón, además de comparar la capacidad predictora de estos, proporcionando información valiosa para futuras investigaciones.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green