Functional time series model identification and diagnosis by means of auto- and partial autocorrelation analysis
From MaRDI portal
Publication:133693
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
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
Cites Work
- Unnamed Item
- Inference for the autocovariance of a functional time series under conditional heteroscedasticity
- Portmanteau Test of Independence for Functional Observations
- Test of independence for functional data
- Inference for functional data with applications
- Curve forecasting by functional autoregression
- Linear processes in function spaces. Theory and applications
- Robust depth-based estimation of the functional autoregressive model
- Functional data analysis.
- Empirical properties of forecasts with the functional autoregressive model
- Estimation and simulation of autoregressive Hilbertian processes with exogenous variables
- White noise testing and model diagnostic checking for functional time series
- A FUNCTIONAL VERSION OF THE ARCH MODEL
- Determining the order of the functional autoregressive model
- Functional Data Analysis with R and MATLAB
- A Simple Test for White Noise in Functional Time Series
- Introduction to Functional Data Analysis
- Tests for Error Correlation in the Functional Linear Model
- On the Prediction of Stationary Functional Time Series
- Computing the distribution of quadratic forms in normal variables
