forecast_ts {dbnR} | R Documentation |
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))
[Package dbnR version 0.7.9 Index]