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

Implementing featurization techniques

Amazon Forecast lets you customize the way you can transform the input datasets by filling in missing values. The presence of missing values in raw data is very common and has a deep impact on the quality of your forecasting model. Indeed, each time a value is missing in your target or related time series data, the true observation is not available to assess the real distribution of historical data.

Although there can be multiple reasons why values are missing, the featurization pipeline offered by Amazon Forecast assumes that you are not able to fill in the values based on your domain expertise and that missing values are actually present in the raw data you ingested into the service. For instance, if we plot the energy consumption of the household with the identifier (ID) MAC002200, we can see that some values are missing at the end of the dataset, as shown in the following screenshot:

Figure 5.15 – Missing values...