Hypothesis Tests for Functional Time Series

Provides an array of white noise hypothesis tests for functional data and related visualizations. These include tests based on the norms of autocovariance operators that are built under both strong and weak white noise assumptions. Additionally, tests based on the spectral density operator and on principal component dimensional reduction are included, which are built under strong white noise assumptions. These methods are described in Kokoszka et al. (2017) , Characiejus and Rice (2019) , and Gabrys and Kokoszka (2007) , respectively.


Reference manual

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1.0.1 by Daniel Petoukhov, a year ago

Report a bug at https://github.com/jimthemadmanlahey/wwntests/issues

Browse source code at https://github.com/cran/wwntests

Authors: Daniel Petoukhov [aut, cre]

Documentation:   PDF Manual  

Task views: Time Series Analysis

GPL-3 license

Imports sde, stats, ftsa, rainbow, MASS, graphics

Suggests testthat, knitr, rmarkdown, fOptions, CompQuadForm, tensorA

See at CRAN