Conformalized Forecasting using Machine Learning models with ARCH effects

Conformalized Forecasting using Machine Learning models with ARCH effects

mlarchf(
  y,
  h = 10L,
  mean_model = forecast::auto.arima,
  model_residuals = forecast::thetaf,
  fit_func = ahead::ridge,
  predict_func = predict,
  type_pi = c("kde", "surrogate", "bootstrap"),
  type_sim_conformalize = c("block-bootstrap", "surrogate", "kde", "bootstrap",
    "fitdistr"),
  ml_method = NULL,
  level = 95,
  B = 250L
)

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)

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

Machine learning method to use (if NULL, uses default methods)

level

Confidence level for prediction intervals

B

Number of bootstrap replications or simulations

Value

A forecast object containing predictions and prediction intervals

Examples

# Example usage will be added