MODELO DE SERIE TEMPORAL PARA PRONOSTICAR LA TEMPERATURA SUPERFICIAL DEL MAR EN LA ZONA COSTERA DE PAITA (PERÚ)

Oscar J.M.Peña Cáceres; Manuel A.More More; Rudy Espinoza Nima; Henry Silva Marchan

doi:10.37467/revtechno.v11.4458

MODELO DE SERIE TEMPORAL PARA PRONOSTICAR LA TEMPERATURA SUPERFICIAL DEL MAR EN LA ZONA COSTERA DE PAITA (PERÚ)

Oscar J.M.Peña Cáceres, Manuel A.More More, Rudy Espinoza Nima, Henry Silva Marchan

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

Resumen

Artificial intelligence techniques have evolved and strengthened, allowing the development of transversal proposals that watch over and safeguard the integrity of the human being. The objective of this study is to develop a time series that forecasts the Sea Surface Temperature (SST) on an average daily scale in the coastal area of Paita, Perú. The methodology used focused on five phases, from data collection to model validation. The results obtained reveal that there is a margin of error of 3.96% on the SST on a weekly average scale and a difference of 0.05 to 1.42, on a daily basis.

Título traducido de la contribución	Time series model to forecast the surface temperature of the sea in the coastal area of Paita (Perú)
Idioma original	Español
Publicación	TECHNO Review. International Technology, Science and Society Review / Revista Internacional de Tecnología, Ciencia y Sociedad
Volumen	11
DOI	https://doi.org/10.37467/revtechno.v11.4458
Estado	Publicada - 29 dic. 2022
Publicado de forma externa	Sí

Nota bibliográfica

Publisher Copyright:
© GKA Ediciones, authors.

Palabras clave

Artificial Neural Network
El Niño Phenomenon
Population
Sea Surface Temperature
Time series

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10.37467/revtechno.v11.4458

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title = "MODELO DE SERIE TEMPORAL PARA PRONOSTICAR LA TEMPERATURA SUPERFICIAL DEL MAR EN LA ZONA COSTERA DE PAITA (PER{\'U})",

abstract = "Artificial intelligence techniques have evolved and strengthened, allowing the development of transversal proposals that watch over and safeguard the integrity of the human being. The objective of this study is to develop a time series that forecasts the Sea Surface Temperature (SST) on an average daily scale in the coastal area of Paita, Per{\'u}. The methodology used focused on five phases, from data collection to model validation. The results obtained reveal that there is a margin of error of 3.96% on the SST on a weekly average scale and a difference of 0.05 to 1.42, on a daily basis.",

keywords = "Artificial Neural Network, El Ni{\~n}o Phenomenon, Population, Sea Surface Temperature, Time series",

author = "C{\'a}ceres, {Oscar J.M.Pe{\~n}a} and More, {Manuel A.More} and Nima, {Rudy Espinoza} and Marchan, {Henry Silva}",

note = "Publisher Copyright: {\textcopyright} GKA Ediciones, authors.",

year = "2022",

month = dec,

day = "29",

doi = "10.37467/revtechno.v11.4458",

language = "Espa{\~n}ol",

volume = "11",

journal = "TECHNO Review. International Technology, Science and Society Review / Revista Internacional de Tecnolog{\'i}a, Ciencia y Sociedad",

issn = "2695-9933",

publisher = "Eagora Science",

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MODELO DE SERIE TEMPORAL PARA PRONOSTICAR LA TEMPERATURA SUPERFICIAL DEL MAR EN LA ZONA COSTERA DE PAITA (PERÚ). / Cáceres, Oscar J.M.Peña; More, Manuel A.More; Nima, Rudy Espinoza et al.
En: TECHNO Review. International Technology, Science and Society Review / Revista Internacional de Tecnología, Ciencia y Sociedad, Vol. 11, 29.12.2022.

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

TY - JOUR

T1 - MODELO DE SERIE TEMPORAL PARA PRONOSTICAR LA TEMPERATURA SUPERFICIAL DEL MAR EN LA ZONA COSTERA DE PAITA (PERÚ)

AU - Cáceres, Oscar J.M.Peña

AU - More, Manuel A.More

AU - Nima, Rudy Espinoza

AU - Marchan, Henry Silva

PY - 2022/12/29

Y1 - 2022/12/29

N2 - Artificial intelligence techniques have evolved and strengthened, allowing the development of transversal proposals that watch over and safeguard the integrity of the human being. The objective of this study is to develop a time series that forecasts the Sea Surface Temperature (SST) on an average daily scale in the coastal area of Paita, Perú. The methodology used focused on five phases, from data collection to model validation. The results obtained reveal that there is a margin of error of 3.96% on the SST on a weekly average scale and a difference of 0.05 to 1.42, on a daily basis.

AB - Artificial intelligence techniques have evolved and strengthened, allowing the development of transversal proposals that watch over and safeguard the integrity of the human being. The objective of this study is to develop a time series that forecasts the Sea Surface Temperature (SST) on an average daily scale in the coastal area of Paita, Perú. The methodology used focused on five phases, from data collection to model validation. The results obtained reveal that there is a margin of error of 3.96% on the SST on a weekly average scale and a difference of 0.05 to 1.42, on a daily basis.

KW - Artificial Neural Network

KW - El Niño Phenomenon

KW - Population

KW - Sea Surface Temperature

KW - Time series

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U2 - 10.37467/revtechno.v11.4458

DO - 10.37467/revtechno.v11.4458

M3 - Artículo

AN - SCOPUS:85144980359

SN - 2695-9933

VL - 11

JO - TECHNO Review. International Technology, Science and Society Review / Revista Internacional de Tecnología, Ciencia y Sociedad

JF - TECHNO Review. International Technology, Science and Society Review / Revista Internacional de Tecnología, Ciencia y Sociedad

ER -

MODELO DE SERIE TEMPORAL PARA PRONOSTICAR LA TEMPERATURA SUPERFICIAL DEL MAR EN LA ZONA COSTERA DE PAITA (PERÚ)

Resumen

Nota bibliográfica

Palabras clave

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Huella

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