Time series forecasting with Spectral Mixture Kernels

Time series modelling is a fundamental yet difficult problem. Forecasting in particular is incredibly challenging and requires strong inductive biases to give good predictions. One powerful framework for encoding inductive biases are kernel functions used with Gaussian Processes (GPs), however, kernels require manual work to embed domain knowledge which might not always be desirable. One might ask if we can learn kernel structures directly from the data, and indeed the answer is yes! Read more