Differentiable Change-point Detection With Temporal Point Processes
Paramita Koley, Harshavardhan Alimi, Shrey Singla, Sourangshu Bhattacharya, Niloy Ganguly, Abir De
Abstract
In this paper, we consider the problem of global change-point detection in event sequence data, where both the event distributions and change-points are assumed to be unknown. For this problem, we propose a Log-likelihood Ratio based Global Change-point Detector, which observes the entire sequence and detects a prespecified number of change-points. Based on the Transformer Hawkes Process (THP), a well-known neural TPP framework, we develop DCPD, a differentiable change-point detector, along with maintaining distinct intensity and mark predictor for each partition. Further, we propose a sliding-window-based extension of DCPD to improve its scalability in terms of the number of events or change-points with minor sacrifices in performance. Experiments on synthetic datasets explore the effects of run-time, relative complexity, and other aspects of distributions on various properties of our changepoint detectors, namely robustness, detection accuracy, scalability, etc. under controlled environments. Finally, we perform experiments on six real-world temporal event sequences collected from diverse domains like health, geographical regions, etc., and show that our methods either outperform or perform comparably with the baselines.
BibTeX
@InProceedings{pmlr-v206-koley23a,
title = {Differentiable Change-point Detection With Temporal Point Processes},
author = {Koley, Paramita and Alimi, Harshavardhan and Singla, Shrey and Bhattacharya, Sourangshu and Ganguly, Niloy and De, Abir},
booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
pages = {6940--6955},
year = {2023},
editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
volume = {206},
series = {Proceedings of Machine Learning Research},
month = {25--27 Apr},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v206/koley23a/koley23a.pdf},
url = {https://proceedings.mlr.press/v206/koley23a.html},
abstract = {In this paper, we consider the problem of global change-point detection in event sequence data, where both the event distributions and change-points are assumed to be unknown. For this problem, we propose a Log-likelihood Ratio based Global Change-point Detector, which observes the entire sequence and detects a prespecified number of change-points. Based on the Transformer Hawkes Process (THP), a well-known neural TPP framework, we develop DCPD, a differentiable change-point detector, along with maintaining distinct intensity and mark predictor for each partition. Further, we propose a sliding-window-based extension of DCPD to improve its scalability in terms of the number of events or change-points with minor sacrifices in performance. Experiments on synthetic datasets explore the effects of run-time, relative complexity, and other aspects of distributions on various properties of our changepoint detectors, namely robustness, detection accuracy, scalability, etc. under controlled environments. Finally, we perform experiments on six real-world temporal event sequences collected from diverse domains like health, geographical regions, etc., and show that our methods either outperform or perform comparably with the baselines.}
}