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

The challenges encountered with multivariate time series data

You can find both single-event and event range anomalies across multivariate time series data. However, the multivariate nature of your problems also gives you more context, and you could start seeing some anomalies because the relationships between the different signals start to diverge from their normal conditions. Let's dive deeper into the following figure (also Figure 8.2):

Figure 8.4 – Event range anomalies

This signal is actually part of a multivariate dataset with 51 sensors collecting data from a pump in a water treatment facility. Let's plot this same signal along with a second one, as follows:

Figure 8.5 – Multivariate time series anomalies

As you can see, when adding this contextual information, you might not label the highlighted area as an anomaly, as this might be the normal behavior of signal 1 (the one at the bottom), when signal 2...