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

How Amazon Forecast leverages automated machine learning

During the training of your first predictor, you disabled AutoPredictor and then allowed the algorithm to choose its default value, which is AutoML. In Automated Machine Learning (AutoML) mode, you don't have to understand which algorithm and configuration to choose from, as Amazon Forecast will run your data against all of the available algorithms (at the time of writing, there are six) and choose the best one.

So, how does Amazon Forecast rank the different algorithms? It computes the weighted quantile loss (wQL; to read more about wQL, please check the Algorithm metrics section) for the forecast types you selected. By default, the selected forecast types are the median (p10) and the boundaries of the 80% confidence interval (p10 and p90). Amazon Forecast computes the wQL for these three quantiles. To determine the winning algorithm, it then takes the average over all of the wQL values that have been calculated: the...