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 predictor evaluation dashboard

When you click on a predictor name on the list of trained models that are available for your dataset group, you get access to a detailed overview screen where you can get a better understanding of the quality of your model. This screen also serves as a reminder of how your training was configured. In this section, we are going to take a detailed look at the different sections of this screen, including the following:

  • Predictor overview
  • Predictor metrics
  • Predictor backtest results

Predictor overview

The Predictor overview section is the first section of the predictor details page:

Figure 4.9 – The results page: Predictor overview

You can find three types of information in this section:

  • Training parameters: These remind you how the predictor was trained. For instance, you can check whether AutoPredictor was set to True or False when training this particular predictor.
  • ...