fpredinterval: FSSA Forecasting Bootstrap Prediction Interval

In Rfssa: Functional Singular Spectrum Analysis

FSSA Forecasting Bootstrap Prediction Interval

Description

Calculate the bootstrap prediction interval for functional singular spectrum analysis (FSSA) forecasting predictions of univariate functional time series (funts) observed over a one-dimensional domain.

Usage

fpredinterval(
  Y,
  O,
  L,
  ntriples,
  Bt,
  h = 1,
  alpha = 0.05,
  method = "recurrent",
  tol = 10^-3
)

Arguments

Y	an object of class funts.
O	a positive integer specifying the training set size.
L	a positive integer specifying the window length.
ntriples	the number of eigentriples to use for forecasts.
Bt	a positive integer specifying the number of bootstrap samples.
h	an integer specifying the forecast horizon.
alpha	a double (0 < alpha < 1) specifying the significance level.
method	a character string: "recurrent" or "vector" forecasting.
tol	a double specifying tolerated error in the approximation.

Value

a list of numeric vectors: point forecast, lower, and upper bounds.

Examples

## Not run: 
data("Callcenter")
pred_interval <- fpredinterval(
  Y = Callcenter, O = 310,
  L = 28, ntriples = 7, Bt = 10000, h = 3
)

# Plot the forecast and prediction interval using ggplot
df <- data.frame(
  x = 1:240,
  y = pred_interval$forecast,
  lower = pred_interval$lower,
  upper = pred_interval$upper
)
require(ggplot2)
# Create the ggplot
ggplot(df, aes(x = x, y = y)) +
  geom_line(linewidth = 1.2) +
  scale_x_continuous(
    name = "Time",
    breaks = c(1, 60, 120, 180, 240),
    labels = c("00:00", "06:00", "12:00", "18:00", "24:00"),
  ) +
  scale_y_continuous(name = "Sqrt of Call Numbers") +
  ggtitle("Prediction Intervals for Jan. 3, 2000") +
  geom_ribbon(aes(ymin = lower, ymax = upper), fill = "darkolivegreen3", alpha = 0.3) +
  theme_minimal()

## End(Not run)

Rfssa documentation built on Oct. 27, 2023, 1:08 a.m.