Classification of robotic sensor streams using non-parametric statistics

We extend our previous work on a classification algorithm for time series. Given time series produced by different underlying generating processes, the algorithm predicts future time series values based on past time series values for each generator. Unlike many algorithms, this algorithm predicts a distribution over future values. This prediction forms the basis for labelling part of a time series with the underlying generator that created it given some labelled exam piles. The algorithm is robust to a wide variety of possible types of changes in signals including mean shifts, amplitude changes, noise changes, period changes, and changes in signal shape. We improve upon the speed of our previous approach and show the utility of the algorithm for discriminating between different states of the robot/environment from robotic sensor signals.