Mihyun Kim, Chi-Kuang Yeh, Gregory Rice, Yuqian Zhao
October 07, 2024
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.
FTSgof is now available on CRAN. You may install it by typing
install.packages("FTSgof")
or you may download the develop version by first installing the R devtools package then run
devtools::install_github("veritasmih/FTSgof")
All the implementation and theory are based on the following papers:
The associated papers are:
Aue, A., Horváth, L., and F. Pellatt, D. (2017). Functional generalized autoregressive conditional heteroskedasticity. Journal of Time Series Analysis, 38, 3-21.
Kim, M., Kokoszka, P., and Rice, G. (2023). White noise testing for functional time series. Statistic Surveys, 17, 119-168.
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.
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.
Rice, G., Wirjanto, T., and Zhao, Y. (2020). Tests for conditional heteroscedasticity of functional data. Journal of Time Series Analysis, 41, 733-758.
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.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4