Applications of complex augmented kernels to wind profile prediction

This paper combines complex signal processing with kernel methods for applications in wind prediction. Specifically, we consider developing least squares kernel algorithms for both complex data and augmented complex data. The augmented complex kernel algorithms have advantages over complex kernel algorithms in both the areas of performance and complexity. Use of kernels also allow implementation of nonlinear algorithms by working in the dual space. We apply our algorithm to wind series time prediction and show that our augmented complex algorithms outperform other complex least square algorithms.