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

Customizing quantiles to suit your business needs

Amazon Forecast generates probabilistic forecasts at different quantiles, giving you prediction intervals over mere point forecasts. Prediction quantiles (or intervals) let Amazon Forecast express the uncertainty of each prediction and give you more information to include in the decision-making process that is linked to your forecast exercise.

As you have seen earlier in this chapter, Amazon Forecast can leverage different forecasting algorithms: each of these algorithms has a different way to estimate probability distributions. For more details about the theoretical background behind probabilistic forecasting, you can refer to the following papers:

  • GluonTS: Probabilistic Time Series Models in Python (https://arxiv.org/pdf/1906.05264.pdf), which gives you some details about the way the ARIMA, ETS, NPTS, and DeepAR+ algorithms generate these predictions' intervals.
  • A Multi-Horizon Quantile Recurrent Forecaster (https...