plot_modeltime_forecast: Interactive Forecast Visualization
In modeltime: The Tidymodels Extension for Time Series Modeling

View source: R/modeltime-forecast-plot.R

plot_modeltime_forecast

R Documentation

Interactive Forecast Visualization

Description

This is a wrapper for timetk::plot_time_series() that generates an interactive (plotly) or static (ggplot2) plot with the forecasted data.

Usage

plot_modeltime_forecast(
  .data,
  .conf_interval_show = TRUE,
  .conf_interval_fill = "grey20",
  .conf_interval_alpha = 0.2,
  .smooth = FALSE,
  .legend_show = TRUE,
  .legend_max_width = 40,
  .facet_ncol = 1,
  .facet_nrow = 1,
  .facet_scales = "free_y",
  .title = "Forecast Plot",
  .x_lab = "",
  .y_lab = "",
  .color_lab = "Legend",
  .interactive = TRUE,
  .plotly_slider = FALSE,
  .trelliscope = FALSE,
  .trelliscope_params = list(),
  ...
)

Arguments

`.data`	A `tibble` that is the output of `modeltime_forecast()`
`.conf_interval_show`	Logical. Whether or not to include the confidence interval as a ribbon.
`.conf_interval_fill`	Fill color for the confidence interval
`.conf_interval_alpha`	Fill opacity for the confidence interval. Range (0, 1).
`.smooth`	Logical - Whether or not to include a trendline smoother. Uses See `smooth_vec()` to apply a LOESS smoother.
`.legend_show`	Logical. Whether or not to show the legend. Can save space with long model descriptions.
`.legend_max_width`	Numeric. The width of truncation to apply to the legend text.
`.facet_ncol`	Number of facet columns.
`.facet_nrow`	Number of facet rows (only used for `.trelliscope = TRUE`)
`.facet_scales`	Control facet x & y-axis ranges. Options include "fixed", "free", "free_y", "free_x"
`.title`	Title for the plot
`.x_lab`	X-axis label for the plot
`.y_lab`	Y-axis label for the plot
`.color_lab`	Legend label if a `color_var` is used.
`.interactive`	Returns either a static (`ggplot2`) visualization or an interactive (`plotly`) visualization
`.plotly_slider`	If `TRUE`, returns a plotly date range slider.
`.trelliscope`	Returns either a normal plot or a trelliscopejs plot (great for many time series) Must have `trelliscopejs` installed.
`.trelliscope_params`	Pass parameters to the `trelliscopejs::facet_trelliscope()` function as a `list()`. The only parameters that cannot be passed are: `ncol`: use `.facet_ncol` `nrow`: use `.facet_nrow` `scales`: use `facet_scales` `as_plotly`: use `.interactive`
`...`	Additional arguments passed to `timetk::plot_time_series()`.

Value

A static ggplot2 plot or an interactive plotly plot containing a forecast

Examples

library(dplyr)
library(lubridate)
library(timetk)
library(parsnip)
library(rsample)

# Data
m750 <- m4_monthly %>% filter(id == "M750")

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.9)

# --- MODELS ---

# Model 1: prophet ----
model_fit_prophet <- prophet_reg() %>%
    set_engine(engine = "prophet") %>%
    fit(value ~ date, data = training(splits))


# ---- MODELTIME TABLE ----

models_tbl <- modeltime_table(
    model_fit_prophet
)

# ---- FORECAST ----

models_tbl %>%
    modeltime_calibrate(new_data = testing(splits)) %>%
    modeltime_forecast(
        new_data    = testing(splits),
        actual_data = m750
    ) %>%
    plot_modeltime_forecast(.interactive = FALSE)

modeltime documentation built on Oct. 23, 2024, 1:07 a.m.