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

Understanding the components of a dataset group

When creating a dataset in Amazon Forecast, you can specify its type. It can be one of the following:

  • A mandatory target time series
  • An optional related time series
  • An optional metadata dataset

These three dataset types help you organize your whole dataset in a format that is compatible with Amazon Forecast.

When you define a dataset group, you also specify its domain: a domain specifies a schema for a common use case but does not impact the algorithms or hyperparameters used at training time. A domain is merely a convenient way to organize your dataset. Amazon Forecast provides the following predefined domains: RETAIL, INVENTORY_PLANNING, EC2_CAPACITY, WORK_FORCE, WEB_TRAFFIC, and METRICS. If none of these suit your use case, you can use the CUSTOM domain and define your custom fields at dataset creation time.

When you want to train a predictor, you create up to three datasets, one of each of the types described...