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

Preparing a dataset for inference

An Amazon Lookout for Equipment inference schedule runs on a tight schedule. You just configured one to be run every 5 minutes. Let's say you just finished this configuration at 10:38. The next times the scheduler will run are 10:40, 10:45, 10:50… until you stop the scheduler. In this section, you will do the following:

  • Learn what happens when a scheduler wakes up.
  • Use CloudShell to prepare some inference data ready to be processed by your scheduler.

Understanding the scheduled inference process

Let's say your scheduler wakes up at 10:40 on September 24, 2021. This scheduler will first look for every CSV file in your input location with a timestamp corresponding to this time in their name: 20210924104000.

As a reminder, the dataset schema you used when creating your initial dataset was the following:

Figure 11.13 – Dataset schema

We put each sensor into its own component. As...