Conformal predictors for online track classification

This paper considers online classification problems where each object to be classified consists of a sequence of measurements, termed here a track. We present an approach that combines ideas from sequential hypothesis testing with those from conformal prediction to address track level outliers - entire measurement sequences that are novel relative to the statistical model. We show with analysis and empirical results that this approach preserves the optimal performance of the underlying sequential hypothesis testing when outliers are absent and provides an error rate guarantee in the presence of contamination by novel tracks.