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README.Rmd
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---
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}
<!-- badges: start -->
[](https://CRAN.R-project.org/package=FTSgof)
[](https://cran.r-project.org/package=FTSgof)
[](https://cran.r-project.org/package=FTSgof)
[](https://github.com/veritasmih/FTSgof)
<!-- badges: end -->
```{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.