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

Monitoring your models

You should also make sure to monitor any potential shift in your data. To do this, you can follow this process:

  1. Build and store a dataset with the training data of all the time series that you want to use to build an anomaly detection model.
  2. Compute the statistical characteristics of each time series (for example, average, standard deviation, and histograms of the distribution of values).
  3. Train your models with these initial datasets and save the performance metrics (how well they capture the anomalies you are interested in).
  4. When new data comes in, compute the same statistical characteristics and compare them with the original values used at training time.
  5. You can display these statistics next to the predictions for your analysts to take the appropriate decisions. This will help them better trust the results generated by Amazon Lookout for Equipment. In particular, visualizing a potential distribution shift from training to inference...