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You're reading from  Machine Learning with the Elastic Stack - Second Edition

Product typeBook
Published inMay 2021
Reading LevelBeginner
PublisherPackt
ISBN-139781801070034
Edition2nd Edition
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Authors (3):
Rich Collier
Rich Collier
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Rich Collier

Rich Collier is a solutions architect at Elastic. Joining the Elastic team from the Prelert acquisition, Rich has over 20 years' experience as a solutions architect and pre-sales systems engineer for software, hardware, and service-based solutions. Rich's technical specialties include big data analytics, machine learning, anomaly detection, threat detection, security operations, application performance management, web applications, and contact center technologies. Rich is based in Boston, Massachusetts.
Read more about Rich Collier

Camilla Montonen
Camilla Montonen
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Camilla Montonen

Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
Read more about Camilla Montonen

Bahaaldine Azarmi
Bahaaldine Azarmi
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Bahaaldine Azarmi

Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
Read more about Bahaaldine Azarmi

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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...

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Machine Learning with the Elastic Stack - Second Edition
Published in: May 2021Publisher: PacktISBN-13: 9781801070034

Authors (3)

author image
Rich Collier

Rich Collier is a solutions architect at Elastic. Joining the Elastic team from the Prelert acquisition, Rich has over 20 years' experience as a solutions architect and pre-sales systems engineer for software, hardware, and service-based solutions. Rich's technical specialties include big data analytics, machine learning, anomaly detection, threat detection, security operations, application performance management, web applications, and contact center technologies. Rich is based in Boston, Massachusetts.
Read more about Rich Collier

author image
Camilla Montonen

Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
Read more about Camilla Montonen

author image
Bahaaldine Azarmi

Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
Read more about Bahaaldine Azarmi