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 5: Customizing Your Predictor Training

In the previous chapter, you trained your first predictor on a household energy consumption dataset. You used the fully automated machine learning (AutoML) approach offered by default by Amazon Forecast, which let you obtain an accurate forecast without any ML or statistical knowledge about time series forecasting.

In this chapter, you will continue to work on the same datasets, but you will explore the flexibility that Amazon Forecast gives you when training a predictor. This will allow you to better understand when and how you can adjust your forecasting approach based on specificities in your dataset or specific domain knowledge you wish to leverage.

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

  • Choosing an algorithm and configuring the training parameters
  • Leveraging hyperparameter optimization (HPO)
  • Reinforcing your backtesting strategy
  • Including holiday and weather data
  • Implementing...