White Noise and Goodness-of-Fit Tests for Functional Time Series

It offers comprehensive tools for the analysis of functional time series data, focusing on white noise hypothesis testing and goodness-of-fit evaluations, alongside functions for simulating data and advanced visualization techniques, such as 3D rainbow plots. These methods are described in Kokoszka, Rice, and Shang (2017) , Yeh, Rice, and Dubin (2023) , Kim, Kokoszka, and Rice (2023) , and Rice, Wirjanto, and Zhao (2020) .

FTSgof: White noise and goodness-of-fit tests for functional time series in R

Mihyun Kim, Gregory Rice, Chi-Kuang Yeh, Yuqian Zhao
University of West Virginia, University of Waterloo, McGill University, University of Sussex

September 25, 2024

Description

Implementation of the robust tools to 1) visualize and perform inference on the autocorrelation structure of time series of functional data objects, and 2) perform goodness-of-fit tests for popular functional time series models.

Installation

Install the R devtools package and run

devtools::install_github("veritasmih/FTSgof")

Reference

Kim, M., Rice, G, Zhao, Y and Yeh, C.-K. (2024+) FTSgof: White noise and goodness-of-fit tests for functional time series in R. arXiv.