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 10: Training and Evaluating a Model

In the previous chapter, you familiarized yourself with a multivariate industrial water pump dataset and learned how to configure data with Amazon Lookout for Equipment. You also ingested your dataset in the service and learned about the main errors that can arise during this step.

In this chapter, you will use the datasets you prepared and ingested previously to train a multivariate anomaly detection model. You will learn how to configure your model training and the impact each parameter can have on your results. You will also develop an understanding of the key drivers that can increase your training duration. At the end of this chapter, we will walk through the evaluation and diagnostics dashboard to give you the right perspective about the quality of the outputs.

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

  • Using your dataset to train a model
  • Organizing your models
  • Choosing a good data split...