One of the main research topics of the TNS group is called Slow Feature Analysis. Slow feature analysis (SFA) is an unsupervised learning method to extract the slowest or smoothest underlying functions or features from a time series. This can be used for dimensionality reduction, regression and classification. In this post we will provide a code example where SFA is applied, to help motivate the method. Then we will go into more detail about the math behind the method and finally provide links to other good resources on the material.