Conformalized Forecasting using Machine Leaning models

mlf(
  y,
  h = 5,
  level = 95,
  lags = 15L,
  fit_func = ahead::ridge,
  predict_func = predict,
  type_pi = c("kde", "surrogate", "blockbootstrap"),
  B = 250L,
  agg = c("mean", "median"),
  seed = 123,
  ...
)

Arguments

y: A numeric vector or time series of class ts
h: Forecasting horizon
level: Confidence level for prediction intervals
lags: Number of lags of the input time series considered in the regression
fit_func: Fitting function (Statistical/ML model). Default is Ridge regression.
predict_func: Prediction function (Statistical/ML model)
type_pi: Type of prediction interval
B: Number of bootstrap replications or number of simulations
agg: "mean" or "median" (aggregation method)
...: additional parameters passed to the fitting function fit_func

Examples


res <- ahead::mlf(USAccDeaths, h=10L, lags=15L, type_pi="surrogate", B=250L)
plot(res)


res <- ahead::mlf(USAccDeaths, fit_func = glmnet::cv.glmnet, h=15L, lags=15L, 
type_pi="kde", B=250L) 
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
plot(res)


(res <- ahead::mlf(USAccDeaths, fit_func = e1071::svm, h=15L, lags=15L, 
type_pi="kde", B=250L)) 
#>          Point Forecast    Lo 95     Hi 95
#> Jan 1979       7885.369 6549.217  9161.012
#> Feb 1979       7994.413 6786.617  9313.743
#> Mar 1979       8288.005 6982.737  9780.596
#> Apr 1979       8944.135 7577.653 10165.216
#> May 1979       8859.941 7410.182 10251.093
#> Jun 1979       9172.114 7644.035 10520.772
#> Jul 1979       9330.366 8002.247 10623.247
#> Aug 1979       9745.846 8494.873 10953.744
#> Sep 1979      10110.171 8881.220 11317.226
#> Oct 1979       9285.259 7846.087 10593.665
#> Nov 1979       8862.986 7561.907  9991.027
#> Dec 1979       8267.721 6741.440  9550.401
#> Jan 1980       7894.621 6491.060  9131.740
#> Feb 1980       7694.869 6419.219  8996.968
#> Mar 1980       8175.129 6808.683  9377.955
plot(res)