An interactive and scalable function for visualizing anomalies in time series data.
Plots are available in interactive plotly (default) and static ggplot2 format.
plot_anomaly_diagnostics(
.data,
.date_var,
.value,
.facet_vars = NULL,
.frequency = "auto",
.trend = "auto",
.alpha = 0.05,
.max_anomalies = 0.2,
.message = TRUE,
.facet_ncol = 1,
.facet_nrow = 1,
.facet_scales = "free",
.facet_dir = "h",
.facet_collapse = FALSE,
.facet_collapse_sep = " ",
.facet_strip_remove = FALSE,
.line_color = "#2c3e50",
.line_size = 0.5,
.line_type = 1,
.line_alpha = 1,
.anom_color = "#e31a1c",
.anom_alpha = 1,
.anom_size = 1.5,
.ribbon_fill = "grey20",
.ribbon_alpha = 0.2,
.legend_show = TRUE,
.title = "Anomaly Diagnostics",
.x_lab = "",
.y_lab = "",
.color_lab = "Anomaly",
.interactive = TRUE,
.trelliscope = FALSE,
.trelliscope_params = list()
)A plotly or ggplot2 visualization
A tibble or data.frame with a time-based column
A column containing either date or date-time values
A column containing numeric values
One or more grouping columns that broken out into ggplot2 facets.
These can be selected using tidyselect() helpers (e.g contains()).
Controls the seasonal adjustment (removal of seasonality).
Input can be either "auto", a time-based definition (e.g. "2 weeks"),
or a numeric number of observations per frequency (e.g. 10).
Refer to tk_get_frequency().
Controls the trend component.
For STL, trend controls the sensitivity of the LOESS smoother, which is used to remove the remainder.
Refer to tk_get_trend().
Controls the width of the "normal" range. Lower values are more conservative while higher values are less prone to incorrectly classifying "normal" observations.
The maximum percent of anomalies permitted to be identified.
A boolean. If TRUE, will output information related to automatic frequency
and trend selection (if applicable).
Number of facet columns.
Number of facet rows (only used for .trelliscope = TRUE)
Control facet x & y-axis ranges. Options include "fixed", "free", "free_y", "free_x"
The direction of faceting ("h" for horizontal, "v" for vertical). Default is "h".
Multiple facets included on one facet strip instead of multiple facet strips.
The separator used for collapsing facets.
Whether or not to remove the strip and text label for each facet.
Line color.
Line size.
Line type.
Line alpha (opacity). Range: (0, 1).
Color for the anomaly dots
Opacity for the anomaly dots. Range: (0, 1).
Size for the anomaly dots
Fill color for the acceptable range
Fill opacity for the acceptable range. Range: (0, 1).
Toggles on/off the Legend
Plot title.
Plot x-axis label
Plot y-axis label
Plot label for the color legend
If TRUE, returns a plotly interactive plot.
If FALSE, returns a static ggplot2 plot.
Returns either a normal plot or a trelliscopejs plot (great for many time series)
Must have trelliscopejs installed.
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
The plot_anomaly_diagnostics() is a visualization wrapper for tk_anomaly_diagnostics()
group-wise anomaly detection, implements a 2-step process to
detect outliers in time series.
Step 1: Detrend & Remove Seasonality using STL Decomposition
The decomposition separates the "season" and "trend" components from the "observed" values leaving the "remainder" for anomaly detection.
The user can control two parameters: frequency and trend.
.frequency: Adjusts the "season" component that is removed from the "observed" values.
.trend: Adjusts the trend window (t.window parameter from stats::stl() that is used.
The user may supply both .frequency and .trend as time-based durations (e.g. "6 weeks") or
numeric values (e.g. 180) or "auto", which predetermines the frequency and/or trend based on
the scale of the time series using the tk_time_scale_template().
Step 2: Anomaly Detection
Once "trend" and "season" (seasonality) is removed, anomaly detection is performed on the "remainder". Anomalies are identified, and boundaries (recomposed_l1 and recomposed_l2) are determined.
The Anomaly Detection Method uses an inner quartile range (IQR) of +/-25 the median.
IQR Adjustment, alpha parameter
With the default alpha = 0.05, the limits are established by expanding
the 25/75 baseline by an IQR Factor of 3 (3X).
The IQR Factor = 0.15 / alpha (hence 3X with alpha = 0.05):
To increase the IQR Factor controlling the limits, decrease the alpha, which makes it more difficult to be an outlier.
Increase alpha to make it easier to be an outlier.
The IQR outlier detection method is used in forecast::tsoutliers().
A similar outlier detection method is used by Twitter's AnomalyDetection package.
Both Twitter and Forecast tsoutliers methods have been implemented in Business Science's anomalize
package.
CLEVELAND, R. B., CLEVELAND, W. S., MCRAE, J. E., AND TERPENNING, I. STL: A Seasonal-Trend Decomposition Procedure Based on Loess. Journal of Official Statistics, Vol. 6, No. 1 (1990), pp. 3-73.
Owen S. Vallis, Jordan Hochenbaum and Arun Kejariwal (2014). A Novel Technique for Long-Term Anomaly Detection in the Cloud. Twitter Inc.
tk_anomaly_diagnostics(): Group-wise anomaly detection
library(dplyr)
walmart_sales_weekly %>%
group_by(id) %>%
plot_anomaly_diagnostics(Date, Weekly_Sales,
.message = FALSE,
.facet_ncol = 3,
.ribbon_alpha = 0.25,
.interactive = FALSE)
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