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

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:

library(FTSgof)

Exploratory Data Analysis with `fport_eda'

The fport_eda function provides a comprehensive exploratory data analysis for functional time series data.

# 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".

# 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".

# 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)