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Table Of Contents
Time Series Analysis on AWS
By :
Time Series Analysis on AWS
By:
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)
Preface
Section 1: Analyzing Time Series and Delivering Highly Accurate Forecasts with Amazon Forecast
Chapter 1: An Overview of Time Series Analysis
Chapter 2: An Overview of Amazon Forecast
Chapter 3: Creating a Project and Ingesting Your Data
Chapter 4: Training a Predictor with AutoML
Chapter 5: Customizing Your Predictor Training
Chapter 6: Generating New Forecasts
Chapter 7: Improving and Scaling Your Forecast Strategy
Section 2: Detecting Abnormal Behavior in Multivariate Time Series with Amazon Lookout for Equipment
Chapter 8: An Overview of Amazon Lookout for Equipment
Chapter 9: Creating a Dataset and Ingesting Your Data
Chapter 10: Training and Evaluating a Model
Chapter 11: Scheduling Regular Inferences
Chapter 12: Reducing Time to Insights for Anomaly Detections
Section 3: Detecting Anomalies in Business Metrics with Amazon Lookout for Metrics
Chapter 13: An Overview of Amazon Lookout for Metrics
Chapter 14: Creating and Activating a Detector
Chapter 15: Viewing Anomalies and Providing Feedback
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