add_kalman_forecast_model: Add kalman forecast model

In ing-bank/tsforecast: Time Series Forecasting Pipeline

Description

add_kalman_forecast_model is a function to add a single kalman forecast model to a (named) list of forecast models.

Usage

add_kalman_forecast_model(
  fc_models,
  ts_object_train,
  fc_name,
  periods_ahead = 1,
  periods_history = Inf,
  verbose = FALSE,
  log_message = ""
)

Arguments

fc_models	A named list of forecast models, with for each forecast model a list with the model itself and a table with forecast values.
ts_object_train	A time series object, which contains only the training data.
fc_name	A character string specifying the name to be used for the new model that is added to the list of existing forecast models.
periods_ahead	A positive integer value indicating the number of periods to forecast ahead.
periods_history	A positive integer value indicating the number of historic datapoints to use for training, which is only relevant for specific forecast methods such as drift and mean.
verbose	Boolean, which is set to TRUE if status updates are valued, or set to FALSE if they are not.

Value

A named list of forecast models, with for each forecast model a list with the model itself and a table with forecast values.

Examples

ts_object_train <- tstools::initialize_ts_forecast_data(
      data = dummy_gasprice,
      date_col = "year_month",
      col_of_interest = "gasprice",
      group_cols = c("state", "oil_company")
   ) %>%
   dplyr::filter(grouping == "state = New York   &   oil_company = CompanyA") %>%
   tstools::transform_data_to_ts_object()
add_kalman_forecast_model(
   fc_models = list(),
   ts_object_train = ts_object_train,
   fc_name = "fc_kalman_poly",
   periods_ahead = 12,
   verbose = T
)    

ing-bank/tsforecast documentation built on Sept. 18, 2020, 9:40 a.m.