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 14: Creating and Activating a Detector

In the previous chapter, we identified the type of anomaly detection problems that are of interest when dealing with business or operational metrics structured as univariate time series. In this chapter, we are taking a dive into using Amazon Lookout for Metrics with an e-commerce dataset that contains the evolution of the number of views and revenues over the course of a year. This dataset is hosted publicly by Amazon Web Services on the Amazon Lookout for Metrics public sample repository:

https://github.com/aws-samples/amazon-lookout-for-metrics-samples/blob/main/workshops/RI2021/ml_ops/datasets

By the end of this chapter, you will have a good understanding of this dataset and you will know how to ingest it into Amazon Lookout for Metrics and use it for both backtesting and live detection purposes.

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

  • Preparing a dataset for anomaly detection purposes...