Book Image

Time Series Analysis on AWS

By : Michaël Hoarau
Book Image

Time Series Analysis on AWS

By: Michaël Hoarau

Overview of this book

Being a business analyst and data scientist, you have to use many algorithms and approaches to prepare, process, and build ML-based applications by leveraging time series data, but you face common problems, such as not knowing which algorithm to choose or how to combine and interpret them. Amazon Web Services (AWS) provides numerous services to help you build applications fueled by artificial intelligence (AI) capabilities. This book helps you get to grips with three AWS AI/ML-managed services to enable you to deliver your desired business outcomes. The book begins with Amazon Forecast, where you’ll discover how to use time series forecasting, leveraging sophisticated statistical and machine learning algorithms to deliver business outcomes accurately. You’ll then learn to use Amazon Lookout for Equipment to build multivariate time series anomaly detection models geared toward industrial equipment and understand how it provides valuable insights to reinforce teams focused on predictive maintenance and predictive quality use cases. In the last chapters, you’ll explore Amazon Lookout for Metrics, and automatically detect and diagnose outliers in your business and operational data. By the end of this AWS book, you’ll have understood how to use the three AWS AI services effectively to perform time series analysis.
Table of Contents (20 chapters)
1
Section 1: Analyzing Time Series and Delivering Highly Accurate Forecasts with Amazon Forecast
9
Section 2: Detecting Abnormal Behavior in Multivariate Time Series with Amazon Lookout for Equipment
15
Section 3: Detecting Anomalies in Business Metrics with Amazon Lookout for Metrics

Interacting with a detector

In this section, you are going to learn how you can interact with a detector, namely, how to get human-readable notifications and how to provide feedback once an anomaly has been issued.

Delivering readable alerts

If you configured an alert on the live detector you created at the beginning of this chapter (see the Training a continuous detector section for more details), you may have already received some of the alerts as emails, but you may have been surprised by the default format of the alerts. The alert contains a lot of information in a JSON document, which is a great format to connect applications but is not easy to consume for us humans. Here is an example of such a JSON document:

{
  "alertName": "ecommerce-revenue-alert-009a9443-d34a-41af-b782-f6e409db55c2",
  "alertEventId": "arn:aws:lookoutmetrics:eu-west-1:123456789012:Alert:ecommerce-revenue-alert-009a9443-d34a-41af-b782-f6e409db55c2...