forecast_ts: Performs forecasting with the GDBN over a dataset

In dbnR: Dynamic Bayesian Network Learning and Inference

Performs forecasting with the GDBN over a dataset

Description

Given a dbn.fit object, the size of the net and a folded dataset, performs a forecast over the initial evidence taken from the dataset.

Usage

forecast_ts(
  dt,
  fit,
  size = NULL,
  obj_vars,
  ini = 1,
  len = dim(dt)[1] - ini,
  rep = 1,
  num_p = 50,
  print_res = TRUE,
  plot_res = TRUE,
  mode = "exact",
  prov_ev = NULL
)

Arguments

dt	data.table object with the TS data
fit	dbn.fit object
size	number of time slices of the net. Deprecated, will be removed in the future
obj_vars	variables to be predicted
ini	starting point in the dataset to forecast.
len	length of the forecast
rep	number of times to repeat the approximate forecasting
num_p	number of particles in the approximate forecasting
print_res	if TRUE prints the mae and sd metrics of the forecast
plot_res	if TRUE plots the results of the forecast
mode	"exact" for exact inference, "approx" for approximate
prov_ev	variables to be provided as evidence in each forecasting step

Value

a list with the original time series values and the results of the forecast

Examples

size = 3
data(motor)
dt_train <- motor[200:900]
dt_val <- motor[901:1000]
obj <- c("pm_t_0")
net <- learn_dbn_struc(dt_train, size)
f_dt_train <- fold_dt(dt_train, size)
f_dt_val <- fold_dt(dt_val, size)
fit <- fit_dbn_params(net, f_dt_train, method = "mle-g")
res <- suppressWarnings(forecast_ts(f_dt_val, fit, 
        obj_vars = obj, len = 10, print_res = FALSE, plot_res = FALSE))

dbnR documentation built on Oct. 5, 2022, 1:07 a.m.