Optimize Spline Models for Time Series Resampling

Internal function used in zoo_resample(). It finds optimal df parameter of a smoothing spline model y ~ x fitted with stats::smooth.spline() that minimizes the root mean squared error (rmse) between observations and predictions, and returns a model fitted with such df.

Usage

utils_optimize_spline(x = NULL, y = NULL, max_complexity = FALSE)

Arguments

x

(required, numeric vector) predictor, a time vector coerced to numeric. Default: NULL

y

(required, numeric vector) response, a column of a zoo object. Default: NULL

max_complexity

(required, logical). If TRUE, RMSE optimization is ignored, and the model of maximum complexity is returned. Default: FALSE

Value

Object of class "smooth.spline".

See also

Other tsl_processing_internal: utils_drop_geometry(), utils_global_scaling_params(), utils_optimize_loess(), utils_rescale_vector()

Examples

#zoo time series
xy <- zoo_simulate(
  cols = 1,
  rows = 30
)

#optimize splines model
m <- utils_optimize_spline(
  x = as.numeric(zoo::index(xy)), #predictor
  y = xy[, 1] #response
)

print(m)
#> Call:
#> stats::smooth.spline(x = model_df$x, y = model_df$y, df = complexity_value, 
#>     all.knots = TRUE)
#> 
#> Smoothing Parameter  spar= -0.9835838  lambda= 8.196849e-16 (26 iterations)
#> Equivalent Degrees of Freedom (Df): 30
#> Penalized Criterion (RSS): 3.436391e-19
#> GCV: 0.04191323

#plot observation
plot(
  x = zoo::index(xy),
  y = xy[, 1],
  col = "forestgreen",
  type = "l",
  lwd = 2
  )

#plot prediction
points(
  x = zoo::index(xy),
  y = stats::predict(
    object = m,
    x = as.numeric(zoo::index(xy))
  )$y,
  col = "red"
)