  In this paper, we propose a kernel principal component analysis model for
multi-variate time series forecasting, where the training and prediction
schemes are derived from the multi-view formulation of Restricted Kernel
Machines. The training problem is simply an eigenvalue decomposition of the
summation of two kernel matrices corresponding to the views of the input and
output data. When a linear kernel is used for the output view, it is shown that
the forecasting equation takes the form of kernel ridge regression. When that
kernel is non-linear, a pre-image problem has to be solved to forecast a point
in the input space. We evaluate the model on several standard time series
datasets, perform ablation studies, benchmark with closely related models and
discuss its results.
