We uploaded on arxiv a new preprint, where we establish, through a relaxation argument, a connection between computationnal geometry and changepoint detection via functional pruning (yes, it’s another focus extension). Via this link we show that it’s possible to extend functional pruning to multidimensional cases (exactly), and we provide a comprehensive description of the conditions required for functional pruning to be applicable :)
We have recently pushed two new FOCuS extensions to arXiv.
NP-FOCuS: This is a non-parametric version of FOCuS used to detect a change-in-distribution. The algorithm combines previous ideas that we explored in non-parametric changepoint detection with functional pruning to improve both detection power and computational complexity. It works for a variety of non-standard scenarios and is perfect for cases where the nature is unknown a priori! You can find the preprint here.
Our latest online nonparametric changepoint detection algorithm just appeared in CSDA. An R package implementing the method is available on Github at the following link.
We present a new algorithm for online changepoint detection. A preprint is available on arXiv At this link.