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

Reviewing anomalies from a trained detector

In this section, we are going to review the different dashboards Amazon Lookout for Metrics provides to help you understand the state of your detectors and the anomalies they detected.

Detector details dashboard

In the previous chapter, you trained a detector in backtest mode and you also built a second one using continuous mode. Once a backtest job is complete, you will see the detector status change on its dashboard as follows:

Figure 15.5 – Completed backtest status

On this main dashboard, you can see a Backtest complete status under the Activate detector step. This means that your historical data has been analyzed. For a continuous detector such as the one you configured and trained in the previous section, you will instead see Learning… and then Activated.

If you scroll down to the bottom of this screen, you will have more details about the backtest job under the Backtest data properties...