Multi-view Kernel PCA for Time series Forecasting
This work addresses forecasting challenges for time series analysts, but it appears incremental as it builds on existing multi-view and kernel methods.
The paper tackles multivariate time series forecasting by proposing a kernel PCA model derived from multi-view Restricted Kernel Machines, showing that with a linear output kernel it reduces to kernel ridge regression and evaluating it on standard datasets.
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.