Convert ffc_gam forecasts to fable object

Usage

# S3 method for class 'ffc_gam'
as_fable(
  x,
  newdata,
  forecasts = NULL,
  response = NULL,
  model = "ETS",
  key_vars = NULL,
  ...
)

Arguments

x: An ffc_gam object used to generate forecasts
newdata: A data.frame containing the forecast data with the response variable
forecasts: Optional pre-computed forecasts from forecast.ffc_gam(). If NULL, forecasts will be generated using the remaining arguments
response: Character string specifying the response variable name. If NULL, automatically detected from the model formula
model: A character string representing the forecasting model to use if generating forecasts. Default is "ETS"
key_vars: Optional character vector specifying grouping variables. If NULL, automatically detected from categorical variables in newdata
...: Additional arguments passed to forecast.ffc_gam() if generating forecasts

Value

A fable x containing forecast distributions and point estimates

Details

Converts forecasting output from ffc_gam xs into a properly formatted fable x that can be used with fabletools functions like autoplot(), accuracy(), and forecast combination methods

Author

Nicholas J Clark

Examples

# Basic usage with automatic detection
library(fable)
#> Loading required package: fabletools
library(tsibble)
#> 
#> Attaching package: ‘tsibble’
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, union
library(dplyr)

# Prepare tourism data
tourism_melb <- tourism |>
  filter(Region == "Melbourne", Purpose == "Visiting") |>
  mutate(quarter = as.numeric(format(Quarter, "%q")),
         time = row_number())

# Split data
train <- tourism_melb |> slice_head(n = 75)
test <- tourism_melb |> slice_tail(n = 5)

# Fit model
mod <- ffc_gam(
  Trips ~ fts(time, mean_only = TRUE, time_k = 50, time_m = 1) +
          fts(quarter, k = 4, time_k = 15, time_m = 1),
  time = "time", data = train, family = tw(), engine = "gam"
)

# Convert to fable with auto-detection
fc_fable <- as_fable(mod, newdata = test, model = "ETS")

# Use fabletools ecosystem
autoplot(fc_fable, train)  # Forecast plot

accuracy(fc_fable, test)   # Accuracy metrics
#> # A tibble: 1 × 13
#>   .model  Region   State Purpose .type    ME  RMSE   MAE   MPE  MAPE  MASE RMSSE
#>   <chr>   <chr>    <chr> <chr>   <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 FFC_ETS Melbour… Vict… Visiti… Test   21.4  91.6  75.5  1.37  9.12   NaN   NaN
#> # ℹ 1 more variable: ACF1 <dbl>
hilo(fc_fable, level = c(80, 95))  # Prediction intervals
#> # A tsibble: 5 x 12 [1Q]
#> # Key:       Region, State, Purpose [1]
#>   Quarter Region    State   Purpose Trips quarter  time       .dist .mean .model
#>     <qtr> <chr>     <chr>   <chr>   <dbl>   <dbl> <int>      <dist> <dbl> <chr> 
#> 1 2016 Q4 Melbourne Victor… Visiti…  804.       4    76 sample[200]  826. FFC_E…
#> 2 2017 Q1 Melbourne Victor… Visiti…  734.       1    77 sample[200]  754. FFC_E…
#> 3 2017 Q2 Melbourne Victor… Visiti…  670.       2    78 sample[200]  763. FFC_E…
#> 4 2017 Q3 Melbourne Victor… Visiti…  824.       3    79 sample[200]  741. FFC_E…
#> 5 2017 Q4 Melbourne Victor… Visiti…  985.       4    80 sample[200]  825. FFC_E…
#> # ℹ 2 more variables: `80%` <hilo>, `95%` <hilo>

# Distribution summaries
fc_fable |>
  summarise(
    mean_forecast = mean(.dist),
    q25 = quantile(.dist, 0.25),
    q75 = quantile(.dist, 0.75)
  )
#> # A tsibble: 5 x 4 [1Q]
#>   Quarter mean_forecast   q25   q75
#>     <qtr>         <dbl> <dbl> <dbl>
#> 1 2016 Q4          826.  770.  873.
#> 2 2017 Q1          754.  715.  801.
#> 3 2017 Q2          763.  715.  804.
#> 4 2017 Q3          741.  694.  788.
#> 5 2017 Q4          825.  777.  881.

# With pre-computed forecasts
forecasts <- forecast(mod, newdata = test, model = "ETS", summary = FALSE)
fc_fable2 <- as_fable(mod, newdata = test, forecasts = forecasts)

# With custom parameters
fc_fable3 <- as_fable(
  mod,
  newdata = test,
  model = "ETS",
  response = "Trips",
  key_vars = c("Region", "State")
)