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

Chapter 11: Scheduling Regular Inferences

In the previous chapter, you trained a model and visualized the events it was able to detect over an evaluation period. Once Amazon Lookout for Equipment has trained a model, you can configure and start an inference scheduler that will run your data against it. This scheduler will wake up regularly, look for CSV files in a location on Amazon S3, open the right ones, and run them with your trained model to predict whether anomalous events are present in your new data. This process is called inference.

In this chapter, you will learn how to manage such schedulers and how to use the predictions obtained. In other words, you will learn how to use a deployed version of your model and use it in production.

In this chapter, we're going to cover the following main topics:

  • Using a trained model
  • Configuring a scheduler
  • Preparing a dataset for inference
  • Extracting the inference results