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

Processing the model diagnostics

When you train an anomaly detection model with Amazon Lookout for Equipment, you can visualize the results obtained over an evaluation period. These results are available in the console and you can also query an API to integrate and further post-process these results for your own needs.

At inference time, the inference scheduler reads new data from an input location on Amazon S3 and outputs the model results in an output location. Each inference execution creates a new directory named after the timestamp at which the scheduler woke up and each directory contains a single file in JSON Lines format. In Chapter 11, Scheduling Regular Inferences, you learned how to locate, download, and interpret the results contained in these files.

In this section, you will use a CloudFormation template that will deploy a CloudWatch dashboard that you can use to visualize training and inference results from Amazon Lookout for Equipment. You will then see how you...