Functional time series model identification and diagnosis by means of auto- and partial autocorrelation analysis

DOI10.1016/j.csda.2020.107108OpenAlexW3092170770MaRDI QIDQ133693

Guillermo Mestre, Gregory Rice, José Portela, Antonio Muñoz San Roque, Estrella Alonso, Guillermo Mestre, Estrella Alonso, Antonio Muñoz San Roque, José Portela, Gregory Rice

Publication date: March 2021

Published in: Computational Statistics & Data Analysis, Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2020.107108

zbMATH Keywords

autocorrelationfunctional time seriespartial autocorrelationmodel diagnosis

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

White noise testing for functional time series, Sieve bootstrapping the memory parameter in long-range dependent stationary functional time series, Estimation of functional ARMA models, Functional spherical autocorrelation: a robust estimate of the autocorrelation of a functional time series, fdaACF

Uses Software

• Rainbow
• fda (R)

Cites Work

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