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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:
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)
#> Hit <Return> to see next plot:
#> Hit <Return> to see next plot:
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)
#> Autocovariance Test
#>
#> Null hypothesis: the series is a weak white noise (sequentially uncorrelated).
#> sample size = 365
#> maximum lag H = 10
#> p-value = 0.000000
fport_wn(Spanish_elec, test = "spherical", H = 10, pplot = TRUE)
#> Spherical Test
#>
#> Null hypothesis: the series is a strong white noise (iid).
#> sample size = 365
#> maximum lag H = 10
#> p-value = 0.000000
# 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")
#> Test for Conditional Heteroscedasticity
#>
#> Null hypothesis: the series is a strong white noise (iid).
#> sample size = 200
#> maximum lag H = 10
#> test type = norm
#> p-value = 0.000000
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)
#> Goodness-of-fit test for FAR(1)
#>
#> Null hypothesis: FAR(1) model is adequate for the series.
#> sample size = 365
#> maximum lag H = 10
#> p-value = 0.000000
# Example with SP500 data
data(sp500)
fport_gof(OCIDR(sp500), test = "arch", M = 1, H = 5)
#> Warning in nloptr::cobyla(x0 = stav, fn = function_to_minimize2, lower =
#> c(rep(10^-20, : The old behavior for hin >= 0 has been deprecated. Please
#> restate the inequality to be <=0. The ability to use the old behavior will be
#> removed in a future release.
#> Goodness-of-fit test for fARCH(1)
#>
#> Null hypothesis: fARCH(1) model is adequate for the series.
#> sample size = 251
#> maximum lag H = 5
#> p-value = 0.010181
fport_gof(OCIDR(sp500), test = "garch", M = 1, H = 5)
#> Warning in nloptr::cobyla(x0 = stav, fn = function_to_minimize2, lower =
#> c(rep(10^-20, : The old behavior for hin >= 0 has been deprecated. Please
#> restate the inequality to be <=0. The ability to use the old behavior will be
#> removed in a future release.
#> Goodness-of-fit test for fGARCH(1,1)
#>
#> Null hypothesis: fGARCH(1,1) model is adequate for the series.
#> sample size = 251
#> maximum lag H = 5
#> p-value = 0.670733
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.