A nonparametric method for gradual change problems with statistical guarantees

Lizhen Nie, Dan L Nicolae

Keywords: Change point detection, gradual change, nonparametric

TL;DR: We propose a method for detecting and localizing gradual changes in sequence data, which is applicable to any data generating model, any data type, requires no prior knowledge, but still comes with theoretical guarantees.

Abstract: We consider the detection and localization of gradual changes in the distribution of a sequence of time-ordered observations. Existing literature focuses mostly on the simpler abrupt setting which assumes a discontinuity jump in distribution, and is unrealistic for some applied settings. We propose a general method for detecting and localizing gradual changes that does not require any specific data generating model, any particular data type, or any prior knowledge about which features of the distribution are subject to change. Despite relaxed assumptions, the proposed method possesses proven theoretical guarantees for both detection and localization.

Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.