Summary
In this chapter, we introduced common sources of time series data and given examples of the different types of time series that they generate. We introduced time aggregation, time alignment, and missing value handling as common preprocessing steps of time series data. Also, we demonstrated how to perform these preprocessing steps in KNIME Analytics Platform.
These preprocessing steps shape the raw time series data from various sources to fulfill the requirements of classic time series analysis methods, create insightful visualizations, and perform efficient and unbiased analysis. As you continue reading this book, you will realize that we refer to the preprocessing steps introduced here frequently. And when you start building your own time series analysis applications, you will likely start with the steps explained in this chapter.
If you’re interested in a workflow example of the preprocessing techniques, you can look at an example workflow on the KNIME Hub ...
