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

Summary

In this chapter, you discovered the many possibilities Amazon Forecast gives you to customize your predictor training to your datasets. You learned how to choose the best algorithm to fit your problem and how to customize different parameters (quantiles, the missing values' filling strategy, and supplementary features usage) to try to improve your forecasting models.

The AutoML capability of Amazon Forecast is a key differentiator when dealing with a new business case or a new dataset. It gives you good directions and reliable results with a fast turnaround. However, achieving higher accuracy to meet your business needs means that you must sometimes be able to override Amazon Forecast decisions by orienting its choice of algorithms, deciding how to process the features of your dataset, or simply requesting a different set of outputs by selecting forecast types that match the way your decision process is run from a business perspective.

In the next chapter, you will...