Short-term rainfall time series prediction with incomplete data

Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Investigación Matemática Aplicada a Control; Argentina.

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Autores principales: Rodriguez Rivero, Cristian, Patiño, Hector Daniel, Pucheta, Julian Antonio
Formato: conferenceObject
Lenguaje:Inglés
Publicado: 2023
Materias:
Benchmark testing
Smoothing methods
Energy associated to series
Nonlinear systems
Datos incompletos
Lluvia
Acceso en línea:http://hdl.handle.net/11086/550244
Aporte de:
Repositorio Digital Universitario (UNC) de Universidad Nacional de Córdoba
id I10-R141-11086-550244
record_format dspace
spelling I10-R141-11086-5502442023-12-20T06:21:34Z Short-term rainfall time series prediction with incomplete data Rodriguez Rivero, Cristian Patiño, Hector Daniel Pucheta, Julian Antonio Benchmark testing Smoothing methods Energy associated to series Nonlinear systems Datos incompletos Lluvia Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Investigación Matemática Aplicada a Control; Argentina. Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Departamento de Ingeniería Electrónica; Argentina. Fil: Patiño, Hector Daniel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Fil: Pucheta, Julian Antonio. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Investigación Matemática Aplicada a Control; Argentina. Fil: Pucheta, Julian Antonio. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Departamento de Ingeniería Electrónica; Argentina. In order to predict short-term times series with incomplete data, a proposed approach is presented based on the energy associated of series. A benchmark of rainfall time series and Mackay Glass (MG) samples are used. An average smoothing technique is adopted to complete the dataset. The structure of the predictor filter is changed taking into account the energy associated of the short series. The H parameter is used to estimate the roughness of the complete series, the real and forecasted one. The next 15 values are used as validation and horizon of the time series presented by series of cumulative monthly historical rainfall from La Sevillana, Cordoba, Argentina and samples of the Mackay Glass (MG) differential equation. The performance of the proposed filter shows that even the short dataset is incomplete, besides a linear smoothing technique employed, the prediction is almost fair. Although the major result shows that the predictor system based on energy associated to series has an optimal performance from several samples of MG equations and, in particular, MG1.6 and SEV rainfall time series, this method provides a good estimation when the short-term series are taken from one point observations. http://dx.doi.org/10.1109/IJCNN.2015.7280315 Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Investigación Matemática Aplicada a Control; Argentina. Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Departamento de Ingeniería Electrónica; Argentina. Fil: Patiño, Hector Daniel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Fil: Pucheta, Julian Antonio. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Investigación Matemática Aplicada a Control; Argentina. Fil: Pucheta, Julian Antonio. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Departamento de Ingeniería Electrónica; Argentina. Sistemas de Automatización y Control 2023-12-19T14:26:28Z 2023-12-19T14:26:28Z 2015 conferenceObject http://hdl.handle.net/11086/550244 eng Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ Electrónico y/o Digital
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic Benchmark testing
Smoothing methods
Energy associated to series
Nonlinear systems
Datos incompletos
Lluvia
spellingShingle Benchmark testing
Smoothing methods
Energy associated to series
Nonlinear systems
Datos incompletos
Lluvia
Rodriguez Rivero, Cristian
Patiño, Hector Daniel
Pucheta, Julian Antonio
Short-term rainfall time series prediction with incomplete data
topic_facet Benchmark testing
Smoothing methods
Energy associated to series
Nonlinear systems
Datos incompletos
Lluvia
description Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Investigación Matemática Aplicada a Control; Argentina.
format conferenceObject
author Rodriguez Rivero, Cristian
Patiño, Hector Daniel
Pucheta, Julian Antonio
author_facet Rodriguez Rivero, Cristian
Patiño, Hector Daniel
Pucheta, Julian Antonio
author_sort Rodriguez Rivero, Cristian
title Short-term rainfall time series prediction with incomplete data
title_short Short-term rainfall time series prediction with incomplete data
title_full Short-term rainfall time series prediction with incomplete data
title_fullStr Short-term rainfall time series prediction with incomplete data
title_full_unstemmed Short-term rainfall time series prediction with incomplete data
title_sort short-term rainfall time series prediction with incomplete data
publishDate 2023
url http://hdl.handle.net/11086/550244
work_keys_str_mv AT rodriguezriverocristian shorttermrainfalltimeseriespredictionwithincompletedata
AT patinohectordaniel shorttermrainfalltimeseriespredictionwithincompletedata
AT puchetajulianantonio shorttermrainfalltimeseriespredictionwithincompletedata
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