A Functional Approach for Building the train_model Components

Add, edit, or remove the components of the train_model function

create_model()

add_input(model.obj, input)

add_methods(model.obj, methods)

remove_methods(model.obj, method_ids)

add_train_method(model.obj, train_method)

add_horizon(model.obj, horizon)

build_model(model.obj)

set_error(model.obj, error)

add_xreg(model.obj, xreg)

add_level(model.obj, level)

Arguments

model.obj	The train_model skeleton, created by the create_model function or edited by add_input, add_methods, remove_methods, add_train_method or add_horizon
input	A univariate time series object (ts class)
methods	A list, defines the models to use for training and forecasting the series. The list must include a sub list with the model type, and the model's arguments (when applicable) and notes about the model. The sub-list name will be used as the model ID. Possible models: arima - model from the stats package auto.arima - model from the forecast package ets - model from the forecast package HoltWinters - model from the stats package nnetar - model from the forecast package tslm - model from the forecast package (note that the 'tslm' model must have the formula argument in the 'method_arg' argument)
method_ids	A character, defines the IDs of the model methods to be remove with the remove_methods function
train_method	A list, defines the train approach, either using a single testing partition (sample out) or use multiple testing partitions (backtesting). The list should include the training method argument, (please see 'details' for the structure of the argument)
horizon	An integer, defines the forecast horizon
error	A character, defines the error metrics to be used to sort the models leaderboard. Possible metric - "MAPE" or "RMSE"
xreg	Optional, a list with two vectors (e.g., data.frame or matrix) of external regressors, one vector corresponding to the input series and second to the forecast itself (e.g., must have the same length as the input and forecast horizon, respectively)
level	An integer, set the confidence level of the prediction intervals

Examples

 
# NOT RUN {
### Building train_model function by adding its different components
# Create a skeleton model
md <- create_model()

class(md)

# Add input
data(USgas)
md <- add_input(model.obj = md, input = USgas)

# Add methods
methods <- list(ets1 = list(method = "ets",
                            method_arg = list(opt.crit = "lik"),
                            notes = "ETS model with opt.crit = lik"),
                ets2 = list(method = "ets",
                            method_arg = list(opt.crit = "amse"),
                            notes = "ETS model with opt.crit = amse"),
                arima1 = list(method = "arima",
                              method_arg = list(order = c(1,1,1),
                                        seasonal = list(order = c(1,0,1))),
                              notes = "SARIMA(1,1,1)(1,0,1)"))

md <- add_methods(model.obj = md, methods = methods)

# Add additional methods
methods2 <- list(arima2 = list(method = "arima",
                              method_arg = list(order = c(2,1,2),
                                     seasonal = list(order = c(1,1,1))),
                              notes = "SARIMA(2,1,2)(1,1,1)"),
                hw = list(method = "HoltWinters",
                          method_arg = NULL,
                          notes = "HoltWinters Model"),
                tslm = list(method = "tslm",
                    method_arg = list(formula = input ~ trend + season),
                    notes = "tslm model with trend and seasonal components"))

md <- add_methods(model.obj = md, methods = methods2)

# Remove methods
md <- remove_methods(model.obj = md, method_ids = c("ets2"))

# Add train method
md <- add_train_method(model.obj = md, train_method = list(partitions = 6,
                                                       sample.out = 12,
                                                       space = 3))


# Set the forecast horizon
md <- add_horizon(model.obj = md, horizon = 12)

# Add the forecast prediction intervals confidence level
md <- add_level(model.obj = md, level = c(90, 95))

### Alternatively, pipe the function with the magrittr package  

library(magrittr)

md <- create_model() %>%
      add_input(input = USgas) %>%
      add_methods(methods = methods) %>%
      add_methods(methods = methods2) %>%
      add_train_method(train_method = list(partitions = 4,
                                           sample.out = 12,
                                           space = 3)) %>%
       add_horizon(horizon = 12) %>%
       add_level(level = c(90, 95))

# Run the model
fc <- md %>% build_model()
# }