plot.windows: Plot validation datasets

In forecastML: Time Series Forecasting with Machine Learning Methods

Description

Plot validation datasets across time.

Usage

## S3 method for class 'windows'
plot(x, lagged_df, show_labels = TRUE, group_filter = NULL, ...)

Arguments

x	An object of class 'windows' from create_windows().
lagged_df	An object of class 'lagged_df' from create_lagged_df().
show_labels	Boolean. If TRUE, show validation dataset IDs on the plot.
group_filter	Optional. A string for filtering plot results for grouped time series (e.g., "group_col_1 == 'A'"). This string is passed to dplyr::filter() internally.
...	Not used.

Value

A plot of the outer-loop nested cross-validation windows of class 'ggplot'.

Examples

# Sampled Seatbelts data from the R package datasets.
data("data_seatbelts", package = "forecastML")

# Example - Training data for 3 horizon-specific models w/ common lags per predictor.
horizons <- c(1, 6, 12)
lookback <- 1:15

data_train <- create_lagged_df(data_seatbelts, type = "train", outcome_col = 1,
                               lookback = lookback, horizon = horizons)

# All historical window lengths of 12 plus any partial windows at the end of the dataset.
windows <- create_windows(data_train, window_length = 12)
plot(windows, data_train)

# Two custom validation windows with different lengths.
windows <- create_windows(data_train, window_start = c(20, 80), window_stop = c(30, 100))
plot(windows, data_train)

forecastML documentation built on July 8, 2020, 7:27 p.m.