Book Image

Machine Learning with the Elastic Stack - Second Edition

By : Rich Collier, Camilla Montonen, Bahaaldine Azarmi
5 (1)
Book Image

Machine Learning with the Elastic Stack - Second Edition

5 (1)
By: Rich Collier, Camilla Montonen, Bahaaldine Azarmi

Overview of this book

Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection. The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with. By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform.
Table of Contents (19 chapters)
1
Section 1 – Getting Started with Machine Learning with Elastic Stack
4
Section 2 – Time Series Analysis – Anomaly Detection and Forecasting
11
Section 3 – Data Frame Analysis

Forecasting theory of operation

The first thing to realize is that the act of invoking a forecast on data is that it is an extension of an existing Anomaly Detection job. In other words, you need to have an Anomaly Detection job configured, and that job needs to have analyzed historical data before you can forecast on that data. This is because the forecasting process uses the models that are created by the Anomaly Detection job. To forecast the data, you need to follow the same steps that were used to create an Anomaly Detection job as described in other chapters. If anomalies were generated by the execution of that job, you can disregard them if your only purpose is to execute forecasting. Once the job has learned on some historical data, the model or models (if the job is configured to analyze data from more than one time series) associated with that job are current and up to date, as represented in the following diagram:

Figure 4.1 – A symbolic representation...