Fit a clustered KWF model for daily load curves — elcf4r_fit_kwf

Cluster dyadically resampled daily curves in a wavelet-energy feature space and use the resulting cluster labels as the grouping structure inside the KWF forecast.

Usage

elcf4r_fit_kwf_clustered(
  segments,
  covariates = NULL,
  target_covariates = NULL,
  wavelet = "la12",
  bandwidth = NULL,
  use_mean_correction = TRUE,
  max_clusters = 10L,
  nstart = 30L,
  cluster_seed = NULL,
  weights = NULL,
  recency_decay = NULL,
  clustering = NULL
)

Arguments

segments: Matrix or data frame of past daily load curves in chronological order.
covariates: Optional data frame with one row per segment.
target_covariates: Optional one-row data frame for the target day.
wavelet: Wavelet filter name passed to wavelets::dwt(). Defaults to "la12".
bandwidth: Optional positive bandwidth for the Gaussian kernel in the underlying KWF fit.
use_mean_correction: Logical; if TRUE, apply the approximation/detail correction in the underlying KWF fit.
max_clusters: Maximum number of candidate clusters considered by the Sugar jump heuristic.
nstart: Number of random starts for kmeans.
cluster_seed: Deprecated and ignored. Clustered KWF now uses deterministic non-random starts.
weights: Optional prior weights passed through to the base KWF fit.
recency_decay: Optional recency prior passed through to the base KWF fit.
clustering: Optional elcf4r_kwf_clusters object. When supplied, the stored clustering model is reused instead of being refit.

Value

An object of class elcf4r_model with method = "kwf_clustered".