Anomaly Detection

In timetk: A Tool Kit for Working with Time Series

knitr::opts_chunk$set(
    message = FALSE,
    warning = FALSE,
    fig.width = 8, 
    fig.height = 4.5,
    fig.align = 'center',
    out.width='95%', 
    dpi = 100
)

# devtools::load_all() # Travis CI fails on load_all()

Anomaly detection is an important part of time series analysis:

1. Detecting anomalies can signify special events
2. Cleaning anomalies can improve forecast error

In this short tutorial, we will cover the plot_anomaly_diagnostics() and tk_anomaly_diagnostics() functions for visualizing and automatically detecting anomalies at scale.

library(dplyr)
library(purrr)
library(timetk)

Data

This tutorial will use the walmart_sales_weekly dataset:

• Weekly
• Sales spikes at various events

walmart_sales_weekly

Anomaly Visualization

Using the plot_anomaly_diagnostics() function, we can interactively detect anomalies at scale.

walmart_sales_weekly %>%
  group_by(Store, Dept) %>%
  plot_anomaly_diagnostics(Date, Weekly_Sales, .facet_ncol = 2)

Automatic Anomaly Detection

To get the data on the anomalies, we use tk_anomaly_diagnostics(), the preprocessing function.

walmart_sales_weekly %>%
  group_by(Store, Dept) %>%
  tk_anomaly_diagnostics(Date, Weekly_Sales)

Learning More

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timetk documentation built on Nov. 2, 2023, 6:18 p.m.