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.

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Table of Contents (20 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
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1
Section 1: Analyzing Time Series and Delivering Highly Accurate Forecasts with Amazon Forecast
Section 1: Analyzing Time Series and Delivering Highly Accurate Forecasts with Amazon Forecast
Free Chapter
2
Chapter 1: An Overview of Time Series Analysis
Chapter 1: An Overview of Time Series Analysis
Technical requirements
What is a time series dataset?
Recognizing the different families of time series
Adding context to time series data
Learning about common time series challenges
Selecting an analysis approach
Typical time series use cases
Summary
3
Chapter 2: An Overview of Amazon Forecast
Chapter 2: An Overview of Amazon Forecast
Technical requirements
What kinds of problems can we solve with forecasting?
What is Amazon Forecast?
How does Amazon Forecast work?
Choosing the right applications
Summary
4
Chapter 3: Creating a Project and Ingesting Your Data
Chapter 3: Creating a Project and Ingesting Your Data
Technical requirements
Understanding the components of a dataset group
Preparing a dataset for forecasting purposes
Creating an Amazon Forecast dataset group
Ingesting data in Amazon Forecast
Summary
5
Chapter 4: Training a Predictor with AutoML
Chapter 4: Training a Predictor with AutoML
Technical requirements
Using your datasets to train a predictor
How Amazon Forecast leverages automated machine learning
Understanding the predictor evaluation dashboard
Exporting and visualizing your predictor backtest results
Summary
6
Chapter 5: Customizing Your Predictor Training
Chapter 5: Customizing Your Predictor Training
Technical requirements
Choosing an algorithm and configuring the training parameters
Leveraging HPO
Reinforcing your backtesting strategy
Including holiday and weather data
Implementing featurization techniques
Customizing quantiles to suit your business needs
Summary
7
Chapter 6: Generating New Forecasts
Chapter 6: Generating New Forecasts
Technical requirements
Generating a forecast
Using lookup to get your items forecast
Exporting and visualizing your forecasts
Generating explainability for your forecasts
Summary
8
Chapter 7: Improving and Scaling Your Forecast Strategy
Chapter 7: Improving and Scaling Your Forecast Strategy
Technical requirements
Deep diving into forecasting model metrics
Understanding your model accuracy
Model monitoring and drift detection
Serverless architecture orchestration
Summary
9
Section 2: Detecting Abnormal Behavior in Multivariate Time Series with Amazon Lookout for Equipment
Section 2: Detecting Abnormal Behavior in Multivariate Time Series with Amazon Lookout for Equipment
10
Chapter 8: An Overview of Amazon Lookout for Equipment
Chapter 8: An Overview of Amazon Lookout for Equipment
Technical requirements
What is Amazon Lookout for Equipment?
What are the different approaches to tackle anomaly detection?
The challenges encountered with multivariate time series data
How does Amazon Lookout for Equipment work?
How do you choose the right applications?
Summary
11
Chapter 9: Creating a Dataset and Ingesting Your Data
Chapter 9: Creating a Dataset and Ingesting Your Data
Technical requirements
Preparing a dataset for anomaly detection purposes
Creating an Amazon Lookout for Equipment dataset
Generating a JSON schema
Creating a data ingestion job
Understanding common ingestion errors and workarounds
Summary
12
Chapter 10: Training and Evaluating a Model
Chapter 10: Training and Evaluating a Model
Technical requirements
Using your dataset to train a model
Model organization best practices
Choosing a good data split between training and evaluation
Evaluating a trained model
Summary
13
Chapter 11: Scheduling Regular Inferences
Chapter 11: Scheduling Regular Inferences
Technical requirements
Using a trained model
Configuring a scheduler
Preparing a dataset for inference
Extracting the inference results
Summary
14
Chapter 12: Reducing Time to Insights for Anomaly Detections
Chapter 12: Reducing Time to Insights for Anomaly Detections
Technical requirements
Improving your model's accuracy
Processing the model diagnostics
Monitoring your models
Orchestrating each step of the process with a serverless architecture
Summary
15
Section 3: Detecting Anomalies in Business Metrics with Amazon Lookout for Metrics
Section 3: Detecting Anomalies in Business Metrics with Amazon Lookout for Metrics
16
Chapter 13: An Overview of Amazon Lookout for Metrics
Chapter 13: An Overview of Amazon Lookout for Metrics
Technical requirements
Recognizing different types of anomalies
What is Amazon Lookout for Metrics?
How does Amazon Lookout for Metrics work?
Identifying suitable metrics for monitoring
Choosing between Lookout for Equipment and Lookout for Metrics
Summary
17
Chapter 14: Creating and Activating a Detector
Chapter 14: Creating and Activating a Detector
Technical requirements
Preparing a dataset for anomaly detection purposes
Creating a detector
Adding a dataset and connecting a data source
Understanding the backtesting mode
Configuring alerts
Summary
18
Chapter 15: Viewing Anomalies and Providing Feedback
Chapter 15: Viewing Anomalies and Providing Feedback
Technical requirements
Training a continuous detector
Reviewing anomalies from a trained detector
Interacting with a detector
Summary
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19
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Chapter 1: An Overview of Time Series Analysis

Time series analysis is a technical domain with a very large choice of techniques that need to be carefully selected depending on the business problem you want to solve and the nature of your time series. In this chapter, we will discover the different families of time series and expose unique challenges you may encounter when dealing with this type of data.

By the end of this chapter, you will understand how to recognize what type of time series data you have and select the best approaches to perform your time series analysis (depending on the insights you want to uncover), and you will understand the use cases that Amazon Forecast, Amazon Lookout for Equipment, and Amazon Lookout for Metrics can help you solve, and which ones they are not suitable for.

You will also have a sound toolbox of time series techniques, Amazon Web Services (AWS) services, and open source Python packages that you can leverage in addition to the three managed services described in detail in this book. Data scientists will also find these tools to be great additions to their time series exploratory data analysis (EDA) toolbox.

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

  • What is a time series dataset?
  • Recognizing the different families of time series
  • Adding context to time series data
  • Learning about common time series challenges
  • Selecting an analysis approach
  • Typical time series use cases
Previous Section
End of Section 1
Next Section