Predict from a brulee_chronos model — predict.brulee

Predict from a brulee_chronos model

Usage

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

Arguments

object: A brulee_chronos object returned by brulee_chronos().
new_data: Optional data frame describing the future window to forecast for. It should contain the id and timestamp columns (when those were supplied at construction) plus any known future covariate values (a subset of the past covariates). The number of rows per series is the number of future time steps to return and may be at most prediction_length; supplying more is an error. When a series has fewer rows than prediction_length, the missing future covariates are treated as unknown and the forecast is truncated to the rows provided. If NULL (the default), the full prediction_length horizon is forecast from the context stored in object. The model is pretrained, so the historical context is always the data passed to brulee_chronos() and is never overridden here.
type: A single string for the type of prediction to return. The default "all" returns both the point forecast (.pred) and the quantile forecast (.pred_quantile). Use "numeric" for only .pred or "quantile" for only .pred_quantile.
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 forecast time step per series (up to nrow(new_data) rows per series, or prediction_length rows when new_data is NULL). Columns depend on type:

<id_column>: The time series identifier. Omitted when the context contains a single series.
.pred: Point forecast, i.e. the median of .pred_quantile. Returned when type is "all" or "numeric".
.pred_quantile: A hardhat::quantile_pred() vector packing all requested quantile levels into a single column. Returned when type is "all" or "quantile".

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, new_data = 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, new_data = 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, new_data = 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()
}
} # }