Predict from a brulee_chronos model

Predict from a brulee_chronos model

Usage

# S3 method for class 'brulee_chronos'
predict(
  object,
  new_data = NULL,
  future_df = NULL,
  prediction_length = NULL,
  quantile_levels = NULL,
  ...
)

Arguments

object

A brulee_chronos object returned by brulee_chronos().

new_data

Optional data frame in the same long format as the data used to build object. It should contain the target and covariate columns named in object, plus the id and timestamp columns when those were supplied at construction. (If the model was built without an id column, every row of new_data is treated as part of the same single series; similarly, if the model was built without a timestamp column, row order is used as the time order.) If NULL (the default), the context stored in object is used.

future_df

Optional data frame with future covariate values. Must contain the id and timestamp columns (when present in the original model) plus any covariate columns to provide for the future window (a subset of the past covariates). Each series must have exactly prediction_length rows.

prediction_length

Number of future time steps to forecast. Defaults to the value stored in object.

quantile_levels

Numeric vector of quantile levels. Defaults to the value stored in object.

...

Not used.

Value

A tibble with one row per future time step per series, in the same order as the rows of new_data (or the stored context). Columns:

<id_column>

The time series identifier. Omitted when the context contains a single series.

.pred

Point forecast, i.e. the median of .pred_quantile.

.pred_quantile

A hardhat::quantile_pred() vector packing all requested quantile levels into a single column.

Examples

pkgs <- c("recipes", "lubridate", "modeldata", "ggplot2")

if (FALSE) { # \dontrun{
if (torch::torch_is_installed() & rlang::is_installed(pkgs)) {
 library(dplyr)
 library(ggplot2)

 n <- nrow(modeldata::Chicago)

 prior_data <- modeldata::Chicago[-((n-13):n),]
 test_data <-
  modeldata::Chicago[(n-13):n,] |>
  mutate(day = lubridate::wday(date, label = TRUE))

 # ------------------------------------------------------------------------------
 # Simple, no covariate model

 mod_1 <-
  brulee_chronos(
   ridership ~ 1,
   data = prior_data,
   # Removing `timestamp_column` does not affect the fit
   timestamp_column = c(date),
   prediction_length = 14)

 pred_1 <- predict(mod_1, test_data)
 pred_1

 pred_1 |>
  bind_cols(test_data) |>
  ggplot(aes(date)) +
  geom_point(aes(y = ridership, col = day)) +
  geom_line(aes(y = .pred)) +
  labs(title = "No covariates: Meh") +
  theme_bw()

 # ------------------------------------------------------------------------------
 # Some covariates via the formula method

mod_2 <-
  brulee_chronos(
   ridership ~ Clark_Lake + Belmont + Harlem + Monroe,
   data = prior_data,
   timestamp_column = c(date),
   prediction_length = 14)

 pred_2 <- predict(mod_2, future_df = test_data)

 pred_2 |>
  bind_cols(test_data) |>
  ggplot(aes(date)) +
  geom_point(aes(y = ridership, col = day)) +
  geom_line(aes(y = .pred)) +
  labs(title = "Four covariates: Pretty good") +
  theme_bw()

 # ------------------------------------------------------------------------------
 # Covariates using recipes

 rec <-
  recipe(ridership ~ ., data = prior_data) |>
  update_role(date, new_role = "time")

 mod_3 <- brulee_chronos(rec, data = prior_data, prediction_length = 14)

 pred_3 <- predict(mod_3, future_df = test_data)

 pred_3 |>
  bind_cols(test_data) |>
  ggplot(aes(date)) +
  geom_point(aes(y = ridership, col = day)) +
  geom_line(aes(y = .pred)) +
  labs(title = "All covariates: Better Saturdays") +
  theme_bw()
}
} # }