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

Leveraging HPO

Training an ML model is a process that consists of finding parameters that will help the model to better deal with real data. When you train your own model without using a managed service such as Amazon Forecast, you can encounter three types of parameters, as follows:

  • Model selection parameters: These are parameters that you have to fix to select a model that best matches your dataset. In this category, you will find the a, b, g, and j parameters from the ETS algorithm, for instance. Amazon Forecast implements these algorithms to ensure that automatic exploration is the default behavior for ETS and ARIMA so that you don't have to deal with finding the best values for these by yourself. For other algorithms (such as NPTS), good default parameters are provided, but you have the flexibility to adjust them based on the inner knowledge of your datasets.
  • Coefficients: These are values that are fitted to your data during the very training of your model. These...