In business-science/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:

Detecting anomalies can signify special events
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

My Talk on High-Performance Time Series Forecasting

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High-Performance Forecasting Systems will save companies MILLIONS of dollars. Imagine what will happen to your career if you can provide your organization a "High-Performance Time Series Forecasting System" (HPTSF System).

I teach how to build a HPTFS System in my High-Performance Time Series Forecasting Course. If interested in learning Scalable High-Performance Forecasting Strategies then take my course. You will learn:

Time Series Machine Learning (cutting-edge) with Modeltime - 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
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Time Series Preprocessing, Noise Reduction, & Anomaly Detection
Feature engineering using lagged variables & external regressors
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