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

Choosing an algorithm and configuring the training parameters

In Chapter 4, Training a Predictor with AutoML, we let Amazon Forecast make all the choices for us and left all the parameters at their default values, including the choice of algorithm. When you follow this path, Amazon Forecast applies every algorithm it knows on your dataset and selects the winning one by looking at which one achieves the best average weighted absolute percentage error (WAPE) metric in your backtest window (if you kept the default choice for the optimization metric to be used).

At the time of writing this chapter, Amazon Forecast knows about six algorithms. The AutoML process is great when you don't have a precise idea about the algorithm that will give the best result with your dataset. The AutoPredictor settings also give you the flexibility to experiment easily with an ensembling technique that will let Amazon Forecast devise the best combination of algorithms for each time series of your dataset...