Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/29934
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dc.contributor.authorGonçalves, A. Manuelapt_PT
dc.contributor.authorCosta, Cláudiapt_PT
dc.contributor.authorCosta, Marcopt_PT
dc.contributor.authorLopes, Sofia O.pt_PT
dc.contributor.authorPereira, Ruipt_PT
dc.date.accessioned2020-12-03T10:54:39Z-
dc.date.available2020-12-03T10:54:39Z-
dc.date.issued2021-
dc.identifier.isbn978-3-030-57421-5pt_PT
dc.identifier.urihttp://hdl.handle.net/10773/29934-
dc.description.abstractPredicting and forecasting weather time series has always been a difficult field of research analysis with a very slow progress rate over the years. The main challenge in this project—The Optimal Challenges in Irrigation (TO CHAIR)—is to study how to manage irrigation problems as an optimal control problem: the daily irrigation problem of minimizing water consumption. For that it is necessary to estimate and forecast weather variables in real time in each monitoring area of irrigation. These time series present strong trends and high-frequency seasonality. How to best model and forecast these patterns has been a long-standing issue in time series analysis. This study presents a comparison of the forecasting performance of TBATS (Trigonometric Seasonal, Box-Cox Transformation, ARMA errors, Trend and Seasonal Components) and regression with correlated errors models. These methods are chosen due to their ability to model trend and seasonal fluctuations present in weather data, particularly in dealing with time series with complex seasonal patterns (multiple seasonal patterns). The forecasting performance is demonstrated through a case study of weather time series: minimum air temperature.pt_PT
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.rightsopenAccesspt_PT
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectIrrigationpt_PT
dc.subjectTemperaturept_PT
dc.subjectTime series modelingpt_PT
dc.subjectForecastingpt_PT
dc.subjectTBATSpt_PT
dc.subjectRegression with correlated errorspt_PT
dc.titleTemperature time series forecasting in The Optimal Challenges in Irrigation (TO CHAIR)pt_PT
dc.typebookPartpt_PT
dc.description.versionpublishedpt_PT
dc.peerreviewedyespt_PT
degois.publication.firstPage423pt_PT
degois.publication.lastPage435pt_PT
degois.publication.titleAdvances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Computational Methods in Applied Sciencespt_PT
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-030-57422-2_27pt_PT
dc.identifier.doi10.1007/978-3-030-57422-2_27pt_PT
dc.identifier.esbn978-3-030-57422-2pt_PT
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