plot_acf_diagnostics: Visualize the ACF, PACF, and CCFs for One or More Time Series
In business-science/timekit: A Tool Kit for Working with Time Series

plot_acf_diagnostics

R Documentation

Visualize the ACF, PACF, and CCFs for One or More Time Series

Description

Returns the ACF and PACF of a target and optionally CCF's of one or more lagged predictors in interactive plotly plots. Scales to multiple time series with group_by().

Usage

plot_acf_diagnostics(
  .data,
  .date_var,
  .value,
  .ccf_vars = NULL,
  .lags = 1000,
  .show_ccf_vars_only = FALSE,
  .show_white_noise_bars = TRUE,
  .facet_ncol = 1,
  .facet_scales = "fixed",
  .line_color = "#2c3e50",
  .line_size = 0.5,
  .line_alpha = 1,
  .point_color = "#2c3e50",
  .point_size = 1,
  .point_alpha = 1,
  .x_intercept = NULL,
  .x_intercept_color = "#E31A1C",
  .hline_color = "#2c3e50",
  .white_noise_line_type = 2,
  .white_noise_line_color = "#A6CEE3",
  .title = "Lag Diagnostics",
  .x_lab = "Lag",
  .y_lab = "Correlation",
  .interactive = TRUE,
  .plotly_slider = FALSE
)

Arguments

`.data`	A data frame or tibble with numeric features (values) in descending chronological order
`.date_var`	A column containing either date or date-time values
`.value`	A numeric column with a value to have ACF and PACF calculations performed.
`.ccf_vars`	Additional features to perform Lag Cross Correlations (CCFs) versus the `.value`. Useful for evaluating external lagged regressors.
`.lags`	A sequence of one or more lags to evaluate.
`.show_ccf_vars_only`	Hides the ACF and PACF plots so you can focus on only CCFs.
`.show_white_noise_bars`	Shows the white noise significance bounds.
`.facet_ncol`	Facets: Number of facet columns. Has no effect if using `grouped_df`.
`.facet_scales`	Facets: Options include "fixed", "free", "free_y", "free_x"
`.line_color`	Line color. Use keyword: "scale_color" to change the color by the facet.
`.line_size`	Line size (linewidth)
`.line_alpha`	Line opacity. Adjust the transparency of the line. Range: (0, 1)
`.point_color`	Point color. Use keyword: "scale_color" to change the color by the facet.
`.point_size`	Point size
`.point_alpha`	Opacity. Adjust the transparency of the points. Range: (0, 1)
`.x_intercept`	Numeric lag. Adds a vertical line.
`.x_intercept_color`	Color for the x-intercept line.
`.hline_color`	Color for the y-intercept = 0 line.
`.white_noise_line_type`	Line type for white noise bars. Set to 2 for "dashed" by default.
`.white_noise_line_color`	Line color for white noise bars. Set to `tidyquant::palette_light()` "steel blue" by default.
`.title`	Title for the plot
`.x_lab`	X-axis label for the plot
`.y_lab`	Y-axis label for the plot
`.interactive`	Returns either a static (`ggplot2`) visualization or an interactive (`plotly`) visualization
`.plotly_slider`	If TRUE, returns a plotly x-axis range slider.

Details

Simplified ACF, PACF, & CCF

We are often interested in all 3 of these functions. Why not get all 3+ at once? Now you can.

ACF - Autocorrelation between a target variable and lagged versions of itself
PACF - Partial Autocorrelation removes the dependence of lags on other lags highlighting key seasonalities.
CCF - Shows how lagged predictors can be used for prediction of a target variable.

Lag Specification

Lags (.lags) can either be specified as:

A time-based phrase indicating a duraction (e.g. ⁠2 months⁠)
A maximum lag (e.g. .lags = 28)
A sequence of lags (e.g. .lags = 7:28)

Scales to Multiple Time Series with Groups

The plot_acf_diagnostics() works with grouped_df's, meaning you can group your time series by one or more categorical columns with dplyr::group_by() and then apply plot_acf_diagnostics() to return group-wise lag diagnostics.

Special Note on Groups

Unlike other plotting utilities, the .facet_vars arguments is NOT included. Use dplyr::group_by() for processing multiple time series groups.

Calculating the White Noise Significance Bars

The formula for the significance bars is +2/sqrt(T) and -2/sqrt(T) where T is the length of the time series. For a white noise time series, 95% of the data points should fall within this range. Those that don't may be significant autocorrelations.

Value

A static ggplot2 plot or an interactive plotly plot

Examples

library(dplyr)
library(ggplot2)


# Apply Transformations
# - Differencing transformation to identify ARIMA & SARIMA Orders
m4_hourly %>%
    group_by(id) %>%
    plot_acf_diagnostics(
        date, value,               # ACF & PACF
        .lags = "7 days",          # 7-Days of hourly lags
        .interactive = FALSE
    )

# Apply Transformations
# - Differencing transformation to identify ARIMA & SARIMA Orders
m4_hourly %>%
    group_by(id) %>%
    plot_acf_diagnostics(
        date,
        diff_vec(value, lag = 1), # Difference the value column
        .lags        = 0:(24*7),   # 7-Days of hourly lags
        .interactive = FALSE
    ) +
    ggtitle("ACF Diagnostics",  subtitle = "1st Difference")

# CCFs Too!
walmart_sales_weekly %>%
    select(id, Date, Weekly_Sales, Temperature, Fuel_Price) %>%
    group_by(id) %>%
    plot_acf_diagnostics(
        Date, Weekly_Sales,                        # ACF & PACF
        .ccf_vars    = c(Temperature, Fuel_Price), # CCFs
        .lags        = "3 months", # 3 months of weekly lags
        .interactive = FALSE
    )

business-science/timekit documentation built on Feb. 2, 2024, 2:51 a.m.