Time Series Prediction for Relational and Kernel Data

This Matlab (R) toolbox provides algorithms to predict the future location of some object in a kernel / distance embedding space. This permits to apply time series prediction to non-vectorial data, such as sequences, trees and graphs. The input for this toolbox are time series of relational or kernel data given as distance or kernel matrices and successor mappings. The output are affine coefficients of training data points, which can be used to locate the predicted point relative to the training data or new data and apply other relational or kernel-based approaches on the predicted point. In more detail, this toolbox implements kernel regression (Nadaraya-Watson regression), Gaussian Processes and the robust Bayesian Committee machine and provides a demo script demonstrating the function of this toolbox.