ftsgof

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

https://github.com/veritasmih/ftsgof

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

README.Rmd

---
title: "FTSgof: White noise and goodness-of-fit tests for functional time series in R"
author: |
  | *Mihyun Kim, Chi-Kuang Yeh, Gregory Rice, Yuqian Zhao*
date: "*`r format(Sys.time(), '%B %d, %Y')`*"
output: github_document
---

\newcommand{\cov}{\mathbb{c}cov}


[![CRAN
status](https://www.r-pkg.org/badges/version/FTSgof)](https://CRAN.R-project.org/package=FTSgof)
[![CRAN
download](https://cranlogs.r-pkg.org/badges/grand-total/FTSgof?color=blue)](https://cran.r-project.org/package=FTSgof)
[![](https://cranlogs.r-pkg.org/badges/FTSgof)](https://cran.r-project.org/package=FTSgof)
[![](https://img.shields.io/github/languages/code-size/veritasmih/FTSgof.svg)](https://github.com/veritasmih/FTSgof)





```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

### 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

*FTSgof* is now available on [CRAN](https://cran.r-project.org/). You may install it by typing

```r
install.packages("FTSgof")
```
or you may download the develop version by first installing the **R** [**`devtools`**](https://CRAN.R-project.org/package=devtools) package then run

```r
devtools::install_github("veritasmih/FTSgof")
```
### TODO

- [x] Add a vignette
- [ ] Add descriptions and examples in README

### Reference

All the implementation and theory are based on the following papers:

* 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. *Under review*.

The associated papers are: 

1. Aue, A., Horváth, L., and F. Pellatt, D. (2017). Functional generalized autoregressive conditional heteroskedasticity. *Journal of Time Series Analysis*, 38, 3-21.


2. Kim, M., Kokoszka, P., and Rice, G. (2023). White noise testing for functional time series. *Statistic Surveys*, 17, 119-168.

3. Kokoszka, P., Rice, G., and Shang, H. L. (2017). Inference for the autocovariance of a functional time series under conditional heteroscedasticity. *Journal of Multivariate Analysis*, 162, 32-50.

4. Mestre, G., Portela, J., Rice, G., San Roque, A. M., and Alonso, E. (2021). Functional time series model identification and diagnosis by means of auto-and partial autocorrelation analysis. *Computational statistics & data analysis*, 155, 107108.

5. Rice, G., Wirjanto, T., and Zhao, Y. (2020). Tests for conditional heteroscedasticity of functional data. *Journal of Time Series Analysis*, 41, 733-758.

6. Yeh, C. K., Rice, G., and Dubin, J.A. (2023). Functional spherical autocorrelation: A robust estimate of the autocorrelation of a functional time series. *Electronic Journal of Statistics*, 17, 650-687.

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