In Gaussian Process (GP), the kernel (co-variance function) is used to measure the similarity between one point and a given point. There are so many kernel functions for GP, and I wonder how to select a suitable kernel. For instance, if my time-series data are not periodic, should I choose the Squared Exponential (SE) kernel?
In addition, could anyone explain why the SE kernel is so popular as well? what is the feature of this kernel?
Thank you for your help in advance.