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# Conformal changepoint detection in continuous model situations

*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.