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Forecasts and conformal prediction bands as a data frame

as.data.frame.farfima_forecast.Rd

One row per grid point, horizon, band and level, for saving the results or for your own plots and summaries.

Usage

# S3 method for class 'farfima_forecast'
as.data.frame(x, row.names = NULL, optional = FALSE, test = FALSE, ...)

Arguments

x

A forecast from predict().

row.names, optional

Not used; they come from as.data.frame().

test

FALSE for the forecasts after the last validation or test curve; TRUE for the forecasts of the test curves, with the observed values.

...

Not used.

Value

A data frame with one row per grid point, horizon, band and level:

  • band, level, h: the band type, its nominal level and the horizon;

  • target: the time being forecast;

  • grid: the grid point;

  • point_forecast, lower, upper: the point forecast and the edges of the conformal prediction band at that grid point.

With test = TRUE, the rows cover the forecasts of the test curves, with three more columns:

  • origin: the time the forecast was made from;

  • target_test_curve: the position of the target in the test sample;

  • observed: the observed value at that grid point.

See also

predict()

Examples

sim = sim_farfima(n = 200, d = 0.3, p = 1, q = 0, no_grid = 51, seed = 1)
blocks = split_curves(sim)
fit = farfima(blocks$train, blocks$valid, d = 0.3, order = c(1, 0), h = 3,
               trim = 50, verbose = FALSE)
fc = predict(fit, test = blocks$test, verbose = FALSE)
head(as.data.frame(fc))
#>   band level h target grid point_forecast     lower     upper
#> 1   SR   0.8 1     h1    1      -1.333967 -1.366452 -1.301482
#> 2   SR   0.8 1     h1    2      -1.427652 -1.624740 -1.230565
#> 3   SR   0.8 1     h1    3      -1.456614 -1.740898 -1.172330
#> 4   SR   0.8 1     h1    4      -1.497945 -1.863685 -1.132206
#> 5   SR   0.8 1     h1    5      -1.444699 -1.867464 -1.021935
#> 6   SR   0.8 1     h1    6      -1.448035 -1.887083 -1.008986
d = as.data.frame(fc, test = TRUE)
d = subset(d, band == "AC" & level == 0.8)
tapply(d$observed >= d$lower & d$observed <= d$upper, d$h, mean)
#>         1         2         3 
#> 0.7906863 0.7702363 0.7389061