wwntests - Hypothesis Tests for Functional Time Series
Provides a collection of white noise hypothesis tests for
functional time series 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.
Also, this package provides goodness-of-fit tests for
functional autoregressive of order 1 models. These methods are
described in Kokoszka et al. (2017)
<doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019)
<doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007)
<doi:10.1198/016214507000001111>, and Kim et al. (2023) <doi:
10.1214/23-SS143> respectively.