Recurrent Restricted Kernel Machines for Time-series Forecasting
Arun Pandey, Hannes De Meulemeester, Henri De Plaen, Bart De Moor, Johan A. K. Suykens · 2022
In this paper, we propose a novel method for time-series modeling and forecasting.It is based on the temporal formulation of Restricted Kernel Machines leading to a dynamical equation in the latent-variables.Forecasting involves finding the next latent variable and then solving a pre-image problem to predict a new-point in the input space.Further, we benchmark our model on several standard data sets against other well-known time-series models.