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

Generating explainability for your forecasts

Since this chapter was written, Amazon Forecast has also deployed a new feature to provide some level of explainability for your predictions. In the previous paragraph, we walked through an error analysis to understand what can be done to improve the forecast accuracy. In this paragraph, we are going to explore this new explainability feature. Explainability is a set of practices and capabilities that help you understand the predictions made by a statistical, machine learning, or deep learning model. In essence, the goal of explainability is to open what can be perceived as a black box to the end users.

Amazon Forecast computes a specific metric (called the impact score) to quantify the impact (does it increase or decrease a given forecast value?) each attribute of your dataset has on your time series. In this paragraph, we are going to look at how to generate such insights and then how to interpret them.

Note

As this was a rather...