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Visualizing Anomalies in Kibana 7.6

Using Kibana visualizations and dashboards, we can spot anomalies in our data but only if we are very intimately familiar with the data. However, with Kibana’s anomaly detection, we can find unusual data points more quickly and easily than we could by ourselves. Combining the output of anomaly detection machine learning jobs with our visualizations, we can annotate what's normal, and what's not, in real time, without having an intimate knowledge of the dataset. In this hands-on lab, we will explore the annotation ability of the TSVB in Kibana to display anomalous behavior over our time series visualizations.

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Path Info

Level
Clock icon Intermediate
Duration
Clock icon 2h 30m
Last updated
Clock icon Aug 30, 2025

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Table of Contents

  1. Challenge

    Create and run the ecommerce-sales ML job.

    1. Create a single-metric anomaly detection machine learning job for the ecommerce index pattern.
    2. Use the full flights data as the time range.
    3. Configure the job to analyze the sum of products.price.
    4. Configure the bucket span to be 30 minutes.
    5. Configure the job to ignore sparse data.
    6. Set the job ID to ecommerce-sales.
    7. Create and configure the job to run in realtime.

  2. Challenge

    Create and save the Sales Over Time visualization.

    1. Create the .ml-anomalies-shared index pattern in order to access the anomaly data.
    2. Create a new TSVB time-series visualization for the ecommerce index pattern.
    3. Configure the series to calculate the sum of the products.price field, label it as Sales, and display it as a dollar amount with 2 decimal places (example: 1,234.567 as $1,234.56).
    4. Configure the visualization to hide the legend.
    5. Add an annotation that displays a red line with an exclamation triangle icon whenever an anomaly with a record_score greater than or equal to 50 occurs for the ecommerce-sales machine learning job.
    6. Configure the annotation's tooltip to display the record_score, typical, and actual values of the anomaly.
    7. Save the visualization as Sales Over Time.

  3. Challenge

    Add the Sales Over Time visualization to the eCommerce dashboard.

    1. Edit the eCommerce dashboard.
    2. Add the Sales Over Time visualization and place it wherever you like.
    3. Save the dashboard.
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