Automated Kernel Search for Gaussian Processes on Data Streams

Gaussian Processes offer non-parametric, probabilistic models that can be used in numerous fields of data analysis. One major drawback is their lack of adjustability in case of drifting and evolving streaming data, where inherent kernels need to be adapted in an efficient manner. To counteract this issue, we propose a novel automated kernel search method that allows us to incrementally adapt Gaussian Process models to evolving IoT data streams. Our approach, denoted as Adjusting Kernel Search (AKS), offers an efficient alternative to searching for suitable kernels from scratch. We evaluate the AKS algorithm on several IoT datasets and show that our approach is able to achieve higher accuracy with lower run-times compared to previous approaches.