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Functional methods for time series prediction: a nonparametric approach

Author

Listed:
  • Germán Aneiros‐Pérez
  • Ricardo Cao
  • Juan M. Vilar‐Fernández

Abstract

The problem of prediction in time series using nonparametric functional techniques is considered. An extension of the local linear method to regression with functional explanatory variable is proposed. This forecasting method is compared with the functional Nadaraya–Watson method and with finite-dimensional nonparametric predictors for several real‐time series. Prediction intervals based on the bootstrap and conditional distribution estimation for those nonparametric methods are also compared. Copyright (C) 2010 John Wiley & Sons, Ltd.

Suggested Citation

  • Germán Aneiros‐Pérez & Ricardo Cao & Juan M. Vilar‐Fernández, 2011. "Functional methods for time series prediction: a nonparametric approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(4), pages 377-392, July.
    • Handle: RePEc:jof:jforec:v:30:y:2011:i:4:p:377-392
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    File URL: http://hdl.handle.net/10.1002/for.1169
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    Cited by:

    1. Elías, Antonio & Jiménez, Raúl & Shang, Han Lin, 2022. "On projection methods for functional time series forecasting," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    2. repec:cte:wsrepe:24606 is not listed on IDEAS
    3. Dursun Aydın & Ersin Yılmaz, 2021. "Semiparametric modeling of the right-censored time-series based on different censorship solution techniques," Empirical Economics, Springer, vol. 61(4), pages 2143-2172, October.
    4. Karim Benhenni & Ali Hajj Hassan & Yingcai Su, 2024. "The effect of correlated errors on the performance of local linear estimation of regression function based on random functional design," Statistical Papers, Springer, vol. 65(6), pages 3395-3423, August.
    5. Acal, C. & Aguilera, A.M. & Alonso, F.J. & Ruiz-Castro, J.E. & Roldán, J.B., 2024. "Different PCA approaches for vector functional time series with applications to resistive switching processes," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 223(C), pages 288-298.
    6. Ismail Shah & Francesco Lisi, 2020. "Forecasting of electricity price through a functional prediction of sale and purchase curves," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 242-259, March.
    7. Pedro Delicado & Philippe Vieu, 2017. "Choosing the most relevant level sets for depicting a sample of densities," Computational Statistics, Springer, vol. 32(3), pages 1083-1113, September.
    8. Goia, Aldo, 2012. "A functional linear model for time series prediction with exogenous variables," Statistics & Probability Letters, Elsevier, vol. 82(5), pages 1005-1011.

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