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

Summary

Amazon Lookout for Equipment is an AI-/ML-managed service running in the cloud. It leverages multiple algorithms to perform anomaly detection on multivariate datasets while abstracting away all the ML decisions you need to take when building your own custom models (for example, questions such as How do I set the threshold to actually capture the anomalies I'm interested in?).

The service is also fully unsupervised. This means that you do not need to spend valuable time to label massive amounts of multivariate time series data. Amazon Lookout for Equipment makes it easy to build whole farms of models that can be applied to each of your individual assets. This allows the service to learn the specific behavior that each asset has developed over the course of the year depending on how it has been manufactured, operated, and maintained.

In this chapter, you learned about the many approaches multivariate anomaly detection can take and the challenges Amazon Lookout for...