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Deseasonalization Methods in Seasonal Streamflow Series Forecasting

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hugo Siqueira & Yara de Souza Tadano & Thiago Antonini Alves & Romis Attux & Christiano Lyra Filho, 2020. "Deseasonalization Methods in Seasonal Streamflow Series Forecasting," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1551-1560, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_159
    DOI: 10.1007/978-3-030-41862-5_159
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