Conformal changepoint detection in continuous model situations

Ilia Nouretdinov, Vladimir Vovk, Alex Gammerman
Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 152:300-302, 2021.

Abstract

Conformal prediction provides a way of testing the IID assumption, which is the standard assumption in machine learning. A natural question is whether this way of testing is efficient. A typical situation where the IID assumption is broken is the existence of a changepoint at which the distribution of the data changes. We study the case of a change from one continuous distribution to another with both distributions belonging to standard parametric families. Our conclusion is that the conformal approach to testing the IID assumption is efficient, at least to some degree.

Cite this Paper

BibTeX
@InProceedings{pmlr-v152-nouretdinov21a, title = {Conformal changepoint detection in continuous model situations}, author = {Nouretdinov, Ilia and Vovk, Vladimir and Gammerman, Alex}, booktitle = {Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications}, pages = {300--302}, year = {2021}, editor = {Carlsson, Lars and Luo, Zhiyuan and Cherubin, Giovanni and An Nguyen, Khuong}, volume = {152}, series = {Proceedings of Machine Learning Research}, month = {08--10 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v152/nouretdinov21a/nouretdinov21a.pdf}, url = {https://proceedings.mlr.press/v152/nouretdinov21a.html}, abstract = {Conformal prediction provides a way of testing the IID assumption, which is the standard assumption in machine learning. A natural question is whether this way of testing is efficient. A typical situation where the IID assumption is broken is the existence of a changepoint at which the distribution of the data changes. We study the case of a change from one continuous distribution to another with both distributions belonging to standard parametric families. Our conclusion is that the conformal approach to testing the IID assumption is efficient, at least to some degree.} }
Endnote
%0 Conference Paper %T Conformal changepoint detection in continuous model situations %A Ilia Nouretdinov %A Vladimir Vovk %A Alex Gammerman %B Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2021 %E Lars Carlsson %E Zhiyuan Luo %E Giovanni Cherubin %E Khuong An Nguyen %F pmlr-v152-nouretdinov21a %I PMLR %P 300--302 %U https://proceedings.mlr.press/v152/nouretdinov21a.html %V 152 %X Conformal prediction provides a way of testing the IID assumption, which is the standard assumption in machine learning. A natural question is whether this way of testing is efficient. A typical situation where the IID assumption is broken is the existence of a changepoint at which the distribution of the data changes. We study the case of a change from one continuous distribution to another with both distributions belonging to standard parametric families. Our conclusion is that the conformal approach to testing the IID assumption is efficient, at least to some degree.
APA
Nouretdinov, I., Vovk, V. & Gammerman, A.. (2021). Conformal changepoint detection in continuous model situations. Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 152:300-302 Available from https://proceedings.mlr.press/v152/nouretdinov21a.html.

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