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

Model organization best practices

Amazon Lookout for Equipment includes the following hierarchy of artifacts within a given AWS account:

  • The dataset is the highest level of the hierarchy; a dataset is defined by an immutable data schema that's defined at creation time.
  • When defining the data schema, the different tags can be regrouped in different components: a component must match a folder in your S3 training dataset.
  • Each dataset can be used to train multiple models and each model can use all the sensors available in the dataset (as defined in the schema) or only a selection of those.
  • The lower level of this hierarchy is the sensor time series (also called tag, signal, or field).

    Note

    A higher level of this hierarchy is the AWS account/user. Although more heavy lifting will be required to set up the appropriate permission, you can build a solution where multiple AWS accounts would use Amazon Lookout for Equipment across your organization, depending on their...