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

Understanding your model accuracy

Some items might be more important than others in a dataset. In retail forecasting, 20% of the sales often accounts for 80% of the revenue, so you might want to ensure you have a good forecast accuracy for your top-moving items (as the others might have a very small share of the total sales most of the time). In every use case, optimizing accuracy for your critical items is important: if your dataset includes several segments of items, properly identifying them will allow you to adjust your forecast strategy.

In this section, we are going to dive deeper into the forecast results of the first predictor you trained in Chapter 4, Training a Predictor with AutoML. In Chapter 6, Generating New Forecasts, I concatenated all the forecast files associated with the AutoML model trained in Chapter 4, Training a Predictor with AutoML. Click on the following link to download my Excel file and follow along with my analysis:

https://packt-publishing-timeseries...