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

How does Amazon Lookout for Metrics work?

In this section, you will learn how Amazon Lookout for Metrics works by first looking at the different concepts manipulated by the service. You will then dive deeper into how these concepts are orchestrated together to build detectors (this is what Lookout for Metrics calls its anomaly detection models). This section will then end with an overview of the pricing model used by this service.

Key concept definitions

To build models able to spot anomalies in your data, Amazon Lookout for Metrics uses the following concepts and resources:

  • Detector: Amazon Lookout for Metrics trains ML models to detect outliers in your data. Such a model is called a detector in this service. A detector continuously learns from your data so that it gets better at understanding the normal behavior and any expected variability.
  • Datasource: A datasource is a service that provides time series data that a detector can analyze. Each datasource must provide...