Deseasonalization Methods in Seasonal Streamflow Series Forecasting

Hugo Siqueira (), Yara de Souza Tadano (), Thiago Antonini Alves (), Romis Attux () and Christiano Lyra Filho ()
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Hugo Siqueira: Federal University of Technology—Parana
Yara de Souza Tadano: Federal University of Technology—Parana
Thiago Antonini Alves: Federal University of Technology—Parana
Romis Attux: University of Campinas
Christiano Lyra Filho: University of Campinas

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1551-1560 from Springer

Abstract: Abstract This work presents an investigation on the application of three deseasonalization models to monthly seasonal streamflow series forecasting: seasonal difference, moving average, and padronization. The deseasonalization is a mandatory preprocessing step for predicting series that present seasonal behavior. The predictors addressed are the linear periodic autoregressive model and an artificial neural network architecture, the extreme learning machines. The computational results showed that the padronization is the most adequate to deal with this problem.

Keywords: Monthly seasonal streamflow series forecasting; Deseasonalization; Neural networks (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_159

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DOI: 10.1007/978-3-030-41862-5_159

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