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Title: Temperature time series forecasting in The Optimal Challenges in Irrigation (TO CHAIR)
Author: Gonçalves, A. Manuela
Costa, Cláudia
Costa, Marco
Lopes, Sofia O.
Pereira, Rui
Keywords: Irrigation
Time series modeling
Regression with correlated errors
Issue Date: 2021
Publisher: Springer
Abstract: Predicting 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.
Peer review: yes
DOI: 10.1007/978-3-030-57422-2_27
ISBN: 978-3-030-57421-5
Publisher Version:
Appears in Collections:CIDMA - Capítulo de livro
ESTGA - Capítulo de livro
PSG - Capítulo de livro

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