model {fabletools}

Estimate models

Description

Trains specified model definition(s) to a dataset. This function will estimate the a set of model definitions (passed via ...) to each series within .data (as identified by the key structure). The result will be a mable (a model table), which neatly stores the estimated models in a tabular structure. Rows of the data identify different series within the data, and each model column contains all models from that model definition. Each cell in the mable identifies a single model.

Usage

model(.data, ...)

## S3 method for class 'tbl_ts'
model(.data, ..., .safely = TRUE)

Arguments

.data

A data structure suitable for the models (such as a tsibble)

...

Definitions for the models to be used. All models must share the same response variable.

.safely

If a model encounters an error, rather than aborting the process a NULL model will be returned instead. This allows for an error to occur when computing many models, without losing the results of the successful models.

Parallel

It is possible to estimate models in parallel using the future package. By specifying a future::plan() before estimating the models, they will be computed according to that plan.

Progress

Progress on model estimation can be obtained by wrapping the code with progressr::with_progress(). Further customisation on how progress is reported can be controlled using the progressr package.

Examples

library(fable)
library(tsibbledata)

# Training an ETS(M,Ad,A) model to Australian beer production
aus_production %>%
  model(ets = ETS(log(Beer) ~ error("M") + trend("Ad") + season("A")))

# Training a seasonal naive and ETS(A,A,A) model to the monthly 
# "Food retailing" turnover for selected Australian states.
library(dplyr)
progressr::with_progress(
aus_retail %>% 
  filter(
    Industry == "Food retailing",
    State %in% c("Victoria", "New South Wales", "Queensland")
  ) %>% 
  model(
    snaive = SNAIVE(Turnover),
    ets = ETS(log(Turnover) ~ error("A") + trend("A") + season("A")),
  )
)