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

Creating an Amazon Lookout for Equipment dataset

As mentioned in Chapter 8, An Overview of Amazon Lookout for Equipment, a dataset is a convenient way to organize your time series data stored as CSV files. These files are stored in an Amazon S3 bucket and organized in different folders.

Each dataset is a container that can contain one or several components that groups tags together. In S3, each component will be materialized by a folder. You can use datasets and components to organize your sensor data depending on how your industrial pieces of equipment are organized themselves.

For instance, you can use the dataset level to store all the tags from a factory and then each component to group all the tags relative to a given production line (across multiple pieces of equipment) or a given piece of equipment.

Figure 9.15 – Folder structure factory/equipment

In this configuration, each component contains several sensors in the same CSV file. Depending...