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

Deep diving into forecasting model metrics

When training a forecasting model, Amazon Forecast will use different machine learning metrics to measure how good a given model and parameters are at predicting future values of a time series. In this section, we are going to detail what these metrics are, why they are important, and how Amazon Forecast uses them.

In Chapter 4, Training a Predictor with AutoML, you trained your first predictor and obtained the following results:

Figure 7.1 – Results page: Accuracy metrics

Using the dropdown on the top right of this section, you can select the algorithm for which you want to see the results. The results you obtained from your first predictor should be similar to the following ones:

Figure 7.2 – Algorithm metrics comparison

At the top of this table, you can see several metric names, such as wQL[0.1], wQL[0.5], wQL[0.9], WAPE, and RMSE. How are these metrics computed? How...