In dkesada/dbnR: Dynamic Bayesian Network Learning and Inference

knitr::opts_chunk$set(echo = TRUE)

library(dbnR)
data(motor)

dt <- motor
size <- 3
n_rep <- 3
ini_fore <- 30
len_fore <- 100

profvis

library(profvis)

main_forecast_profile <- function(dt, size, n_rep, ini_fore, len_fore){
  profvis({
    dt_train <- dt[1:2500]
    dt_test <- dt[2501:3000]
    f_dt_train <- fold_dt(dt_train, size)
    f_dt_test <- fold_dt(dt_test, size)

    net <- learn_dbn_struc(dt_train, size = size)
    fit <- fit_dbn_params(net, f_dt_train)
    res_fore <- forecast_ts(f_dt_test, fit, obj_vars = "pm_t_0", rep = n_rep,
                      ini = ini_fore, len = len_fore)
  })

  return(list(net = net, fit = fit, res_fore = res_fore))
}

res_m <- main_forecast_profile(motor, 3, 3, 30, 100)

dkesada/dbnR documentation built on June 19, 2024, 6:45 p.m.