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 9: Creating a Dataset and Ingesting Your Data

In the previous chapter, you learned about anomaly detection problems and some ways to tackle them. You also had an overview of Amazon Lookout for Equipment, an AI-/ML-managed service designed to build anomaly detection problems in multivariate, industrial time series data.

The goal of this chapter is to teach you how to create and organize multivariate datasets, how to create a JSON schema to prepare the dataset ingestion, and how to trigger a data ingestion job pointing to the S3 bucket where your raw data is stored.

In addition, you will also have a high-level understanding of all the heavy lifting the service is performing on your behalf to save as much data preparation effort as possible (imputation, time series alignment, resampling). You will also understand what kind of errors can be raised by the service and how to work around them.

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

  • Preparing...