Interactive Residuals Visualization — plot_modeltime

This is a wrapper for examining residuals using:

Time Plot: timetk::plot_time_series()
ACF Plot: timetk::plot_acf_diagnostics()
Seasonality Plot: timetk::plot_seasonal_diagnostics()

Usage

plot_modeltime_residuals(
  .data,
  .type = c("timeplot", "acf", "seasonality"),
  .smooth = FALSE,
  .legend_show = TRUE,
  .legend_max_width = 40,
  .title = "Residuals Plot",
  .x_lab = "",
  .y_lab = "",
  .color_lab = "Legend",
  .interactive = TRUE,
  ...
)

Arguments

.data

A tibble that is the output of modeltime_residuals()

.type

One of "timeplot", "acf", or "seasonality". The default is "timeplot".

.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.

.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

...

Additional arguments passed to:

Time Plot: timetk::plot_time_series()
ACF Plot: timetk::plot_acf_diagnostics()
Seasonality Plot: timetk::plot_seasonal_diagnostics()

Value

A static ggplot2 plot or an interactive plotly plot containing residuals vs time

Examples

library(dplyr)
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))
#> Disabling weekly seasonality. Run prophet with weekly.seasonality=TRUE to override this.
#> Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.


# ---- MODELTIME TABLE ----

models_tbl <- modeltime_table(
    model_fit_prophet
)

# ---- RESIDUALS ----

residuals_tbl <- models_tbl %>%
    modeltime_calibrate(new_data = testing(splits)) %>%
    modeltime_residuals()

residuals_tbl %>%
    plot_modeltime_residuals(
        .type = "timeplot",
        .interactive = FALSE
    )