Summary
Although every time series looks alike (a tabular dataset indexed by time), choosing the right tools and approaches to frame a time series problem is critical to successfully leverage ML to uncover business insights.
After reading this chapter, you understand how time series can vastly differ from one another and you should have a good command of the families of preprocessing, transformation, and analysis techniques that can help derive insights from time series datasets. You also have an overview of the different AWS services and open source packages you can leverage to help you in your endeavor. After reading this chapter, you can now recognize how rich this domain is and the numerous options you have to process and analyze your time series data.
In the next three parts of this book, we are going to abstract away most of these choices and options by leveraging managed services that will do most of the heavy lifting for you. However, it is key to have a good command of these concepts to develop the right understanding of what is going on under the hood. This will also help you make the right choices whenever you have to tackle a new use case.
We will start with the most popular time series problem we want to solve with time series forecasting, with Amazon Forecast.