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

Reinforcing your backtesting strategy

In ML, backtesting is a technique used in forecasting to provide the learning process with two datasets, as follows:

  • A training dataset on which the model will be trained
  • A testing dataset on which we will evaluate the performance of the model on data that was not seen during the training phase

As a reminder, here are the different elements of backtesting in Amazon Forecast, as outlined in Chapter 4, Training a Predictor with AutoML:

Figure 5.8 – Backtesting elements

When dealing with time series data, the split must mainly be done on the temporal axis (and, to a lesser extent, on the item population) to prevent any data leak from the past data to the future. This is paramount to make your model robust enough for when it will have to deal with actual production data.

By default, when you leave the default parameter as is (when selecting AutoML or when selecting an algorithm manually), Amazon...