The Extended Kernel Adaptive Autoregressive-Moving-Average Algorithm

In this paper, we proposed the Extended KAARMA algorithm, which substitutes gradient descent (SGD) by the Extended Kalman (EKF) update equations. Comparing with the stochastic gradient descent method, the EKF method provides a higher rate of convergence. By creating more centers in the memory, it can explore the error space in an efficient manner. Besides, the Extended Kalman method can adjust the learning rate automatically. The decreasing learning rate solves the gradient explosion problem, where a gradient clipping technique is needed in the SGD method instead.