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.

翻译：在本文中,我们提出了一个多变时间序列预测的内核主元分析模型,其中培训和预测计划来自限制内核机的多视图配方。培训问题只是两个内核矩阵的相加与输入和输出数据相匹配的二个内核矩阵的精度分解。当输出视图使用线性内核时,显示预测方程式的形式是内核脊回归。当内核为非线性时,必须解决一个预想问题,才能预测输入空间中的某个点。我们评估数个标准时间序列数据集的模型,进行通货膨胀研究,与密切相关的模型基准,并讨论其结果。