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

What are the different approaches to tackle anomaly detection?

Before we dive into Amazon Lookout for Equipment, first, we are going to look at a few definitions. In this section, you will read about the different types of anomalies before getting a high-level overview of the different methods you can use to build your own anomaly detection models.

What is an anomaly?

An anomaly in a time series is usually defined as an observation or sequence of observations that do not follow the expected behavior of the series. For example, you could have point anomalies (in other words, single events that are only recorded at a single timestamp):

Figure 8.1 – Single-event anomalies

Additionally, you might have a sequence of data points that can be viewed as a suspicious event with a longer time range:

Figure 8.2 – Event range anomalies

These sequences of anomalies are more challenging to identify than point anomalies as they...