Conformalized Forecasting using Machine Learning (and statistical) models with ARCH effects

mlarchf(
  y,
  h = 10L,
  mean_model = forecast::auto.arima,
  model_residuals = forecast::meanf,
  fit_func = ahead::ridge,
  predict_func = predict,
  lags_vol = 10L,
  type_pi = c("surrogate", "bootstrap", "kde"),
  type_sim_conformalize = c("surrogate", "block-bootstrap", "bootstrap", "kde",
    "fitdistr"),
  ml_method = NULL,
  level = 95,
  B = 250L,
  ml = TRUE,
  stat_model = NULL,
  n_clusters = 0,
  clustering_dist = "euclidean",
  clustering_method = "kmeans",
  ...
)

Arguments

y: A numeric vector or time series of class ts
h: Forecasting horizon
mean_model: Function to fit the mean model (default: forecast::auto.arima)
model_residuals: Function to model the residuals (default: forecast::thetaf)
fit_func: Fitting function for the variance model (default: ahead::ridge)
predict_func: Prediction function for the variance model (default: predict)
lags_vol: Number of lags for squared residuals regression
type_pi: Type of prediction interval ("kde", "surrogate", or "bootstrap") for volatility modeling
type_sim_conformalize: Type of simulation for conformalization of standardized residuals ("block-bootstrap", "surrogate", "kde", "bootstrap", or "fitdistr")
ml_method: caret package Machine learning method to use, if fit_func and predict_func aren't provided. If NULL, uses fit_func and predict_func. See unique(caret::modelLookup()$model).
level: Confidence level for prediction intervals
B: Number of bootstrap replications or simulations
ml: If TRUE, fit_func and predict_func are used, otherwise a statistical model in stat_model
stat_model: A statistical model, e.g forecast::thetaf or forecast::auto.arima
n_clusters: Number of clusters for residuals (default is 0) for ml == TRUE and ml_method not NULL
clustering_dist: Only if n_clusters > 0. If "euclidean", then mean square error, if "manhattan ", the mean absolute error is used.
clustering_method: Only if n_clusters > 0. If "kmeans", then we have the kmeans clustering method, if "hardcl" we have the On-line Update (Hard Competitive learning) method, and if "neuralgas", we have the Neural Gas (Soft Competitive learning) method. Abbreviations of the method names are accepted.
...: Additional parameters to be passed to stat_model

Value

A forecast object containing predictions and prediction intervals

Examples


y <- fpp2::goog200

# Default model for volatility (Ridge regression for volatility)
(obj_ridge <- ahead::mlarchf(y, h=20L, B=500L))
#>     Point Forecast    Lo 95    Hi 95
#> 201       533.4806 512.7812 549.9381
#> 202       534.7064 515.7697 552.1797
#> 203       538.8649 484.1721 616.3803
#> 204       536.1930 523.4435 551.1034
#> 205       536.8055 520.5741 556.1976
#> 206       537.5393 520.6689 553.1701
#> 207       537.6365 524.3945 548.4499
#> 208       537.4759 511.4850 552.1990
#> 209       539.1986 522.4345 551.1335
#> 210       539.4604 519.2788 557.4779
#> 211       540.2246 514.9235 557.3380
#> 212       542.1635 527.1610 558.9331
#> 213       542.3887 518.5475 567.8483
#> 214       543.2326 524.3450 559.1676
#> 215       543.8326 519.7481 563.2478
#> 216       544.5939 522.5941 563.2784
#> 217       544.6853 533.4650 554.1662
#> 218       547.5403 518.1748 587.8554
#> 219       546.7615 517.4711 571.0961
#> 220       547.3477 528.1812 567.5666
plot(obj_ridge)