Interactive Residuals Visualization
This is a wrapper for examining residuals using:
Time Plot: plot_time_series()
ACF Plot: plot_acf_diagnostics()
Seasonality Plot: plot_seasonal_diagnostics()
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,
...
).data |
A |
.type |
One of "timeplot", "acf", or "seasonality". The default is "timeplot". |
.smooth |
Logical - Whether or not to include a trendline smoother.
Uses See |
.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 |
.interactive |
Returns either a static ( |
... |
Additional arguments passed to:
|
A static ggplot2 plot or an interactive plotly plot containing residuals vs time
library(tidyverse)
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: auto_arima ----
model_fit_arima <- arima_reg() %>%
set_engine(engine = "auto_arima") %>%
fit(value ~ date, data = training(splits))
# ---- MODELTIME TABLE ----
models_tbl <- modeltime_table(
model_fit_arima
)
# ---- RESIDUALS ----
residuals_tbl <- models_tbl %>%
modeltime_calibrate(new_data = testing(splits)) %>%
modeltime_residuals()
residuals_tbl %>%
plot_modeltime_residuals(
.type = "timeplot",
.interactive = FALSE
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