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Short-term forecast improvement of maximum temperature by state-space model approach: the study case of the TO CHAIR project

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  • معلومة اضافية
    • بيانات النشر:
      Springer
    • الموضوع:
      2024
    • Collection:
      Repositório Institucional da Universidade de Aveiro (RIA)
    • نبذة مختصرة :
      In the context of “TO CHAIR” project, this work aims to improve the accuracy of short-term forecasts of maximum air temperature obtained from the https://weatherstack.com/website. The proposed methodology is based on a state-space representation that incorporates the latent process, the state, which is estimated recursively using the Kalman filter. The proposed model linearly and stochastically relates the forecasts from the website (as a covariate) to the observations of the maximum temperature recorded at the study site. The specification of the state-space model is performed using the maximum likelihood method under the assumption of normality of errors, where empirical confidence intervals are presented. In addition, this work also presents a treatment of outliers based on the ratios between the observed maximum temperature and the website forecasts. ; published
    • ISSN:
      1436-3259
    • Relation:
      POCI-01-0145-FEDER-028247; https://link.springer.com/article/10.1007/s00477-022-02290-3; http://hdl.handle.net/10773/34405
    • الرقم المعرف:
      10.1007/s00477-022-02290-3
    • الدخول الالكتروني :
      http://hdl.handle.net/10773/34405
      https://doi.org/10.1007/s00477-022-02290-3
    • Rights:
      openAccess ; https://creativecommons.org/licenses/by/4.0/
    • الرقم المعرف:
      edsbas.F0505D07