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Generalized βARMA model for double bounded time series forecasting

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  • Scher, Vinícius T.
  • Cribari-Neto, Francisco
  • Bayer, Fábio M.

Abstract

The βARMA model is tailored for use with time series that assume values in (0,1). We generalize the model in which both the conditional mean and conditional precision evolve over time. The standard βARMA model, in which precision is constant, is a particular case of our model. The more general model formulation includes a parsimonious submodel for the precision parameter. We present the model conditional log-likelihood function, the conditional score function, and the conditional Fisher information matrix. We use the proposed model to forecast future levels of stored hydroelectric energy and the useful volume of a water reservoir in the South of Brazil. Our results show that more accurate forecasts are typically obtained by allowing the precision parameter to evolve over time.

Suggested Citation

  • Scher, Vinícius T. & Cribari-Neto, Francisco & Bayer, Fábio M., 2024. "Generalized βARMA model for double bounded time series forecasting," International Journal of Forecasting, Elsevier, vol. 40(2), pages 721-734.
    • Handle: RePEc:eee:intfor:v:40:y:2024:i:2:p:721-734
    DOI: 10.1016/j.ijforecast.2023.05.005
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    References listed on IDEAS

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    1. Guilherme Pumi & Taiane Schaedler Prass & Rafael Rigão Souza, 2021. "A dynamic model for double‐bounded time series with chaotic‐driven conditional averages," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 68-86, March.
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    3. Cribari-Neto, Francisco & Scher, Vinícius T. & Bayer, Fábio M., 2023. "Beta autoregressive moving average model selection with application to modeling and forecasting stored hydroelectric energy," International Journal of Forecasting, Elsevier, vol. 39(1), pages 98-109.
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