--- title: "FTSgof" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{FTSgof} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` This vignette provides an overview of how to perform exploratory data analysis, white noise hypothesis testing and the goodness-of-fit tests for functional time series (FTS) data using the functions `fport_eda`, `fport_wn`, `fport_gof`. Functional time series data consists of a sequence of curves, allowing for the analysis of complex data structures over time. First, ensure you have the package installed and loaded: ```{r setup} library(FTSgof) ``` # Exploratory Data Analysis with `fport_eda' The `fport_eda` function provides a comprehensive exploratory data analysis for functional time series data. ```{r setup2} # Load example data data(Spanish_elec) # Daily Spanish electricity price profiles # Perform exploratory data analysis fport_eda(Spanish_elec, H = 20, alpha = 0.05, wwn_bound = FALSE, M = NULL) ``` # White Noise Hypothesis Testing with `fport_wn` The `fport_wn` function computes various white noise tests for functional time series data. The available tests are "autocovariance", "spherical", and "arch". ```{r setup3} # Perform white noise hypothesis testing fport_wn(Spanish_elec, test = "autocovariance", H = 10) fport_wn(Spanish_elec, test = "spherical", H = 10, pplot = TRUE) # Generate fGARCH(1) data for testing yd_garch <- dgp.fgarch(J = 50, N = 200, type = "garch")$garch_mat fport_wn(yd_garch, test = "ch", H = 10, stat_Method = "norm") ``` # Goodness-of-fit Tests with `fport_gof` The `fport_gof` function conducts goodness-of-fit tests for functional time series data. The available tests are "far", "arch", and "garch". ```{r setup4} # Perform goodness-of-fit tests fport_gof(Spanish_elec, test = "far", H = 10) # Example with SP500 data data(sp500) fport_gof(OCIDR(sp500), test = "arch", M = 1, H = 5) fport_gof(OCIDR(sp500), test = "garch", M = 1, H = 5) ```