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

Model monitoring and drift detection

As was made clear in the previous section, in addition to error analysis, you should also make sure to monitor any potential shifts in your data. To do this, you could 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 a predictor.
  2. Compute the statistical characteristics of your dataset at a global level (for example, average, standard deviation, or histograms of the distribution of values).
  3. Compute the statistical characteristics of your dataset at a local level (for each time series).
  4. Train your predictors with these initial datasets and save the performance metrics (wQL, MAPE, and RMSE).
  5. Generate a new forecast based on this predictor; you will only get the predictions here and have no real data to compare them with yet.
  6. When new data comes in, compute the same statistical characteristics and compare them with the original values used at training...