Book Image

Machine Learning with the Elastic Stack

By : Rich Collier, Bahaaldine Azarmi
Book Image

Machine Learning with the Elastic Stack

By: Rich Collier, Bahaaldine Azarmi

Overview of this book

Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.
Table of Contents (12 chapters)

Forecast results

Now that we have run a forecast, we can look in more depth at the results that are generated by the forecasting process. By the way, we can view the results of a previously created forecast at any time in the UI via one of two methods. You can click the Forecast button in the Single Metric Viewer to reveal a list of Previous Forecasts, like so:

Alternatively, you can view them in the Job Management page under the Forecasts tab for that job:

Forecast results built in Kibana have a default lifespan of 14 days. After that, the forecast results are deleted permanently. If a different expiration duration is desired, then the forecast will have to be invoked via the _forecast API endpoint, which will be discussed later, but is documented at https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-forecast.html.

In either case, clicking on the View icon...