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.

model(.data, ...)

# S3 method for 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.

Examples

if (requireNamespace("fable", quietly = TRUE) && requireNamespace("tsibbledata", quietly = TRUE)) { 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) 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")), ) }
#> Warning: 1 error encountered for ets #> [1] .data contains implicit gaps in time. You should check your data and convert implicit gaps into explicit missing values using `tsibble::fill_gaps()` if required.
#> Warning: 3 errors (1 unique) encountered for snaive #> [3] .data contains implicit gaps in time. You should check your data and convert implicit gaps into explicit missing values using `tsibble::fill_gaps()` if required.
#> Warning: 3 errors (1 unique) encountered for ets #> [3] .data contains implicit gaps in time. You should check your data and convert implicit gaps into explicit missing values using `tsibble::fill_gaps()` if required.
#> # A mable: 3 x 4 #> # Key: State, Industry [3] #> State Industry snaive ets #> <chr> <chr> <model> <model> #> 1 New South Wales Food retailing <NULL model> <NULL model> #> 2 Queensland Food retailing <NULL model> <NULL model> #> 3 Victoria Food retailing <NULL model> <NULL model>