Learning Time Series Detection Models from Temporally Imprecise Labels
This addresses the challenge of noisy annotation in time series data for applications like mobile health, though it is incremental as it builds on existing learning frameworks.
The paper tackles the problem of learning time series detection models from temporally imprecise labels, such as noisy timestamps in mobile health data, and proposes a general framework that significantly outperforms alternatives like assuming noise-free labels or using multiple instance learning.
In this paper, we consider a new low-quality label learning problem: learning time series detection models from temporally imprecise labels. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy time stamps corresponding to the occurrence of positive class events. Such temporally imprecise labels commonly occur in areas like mobile health research where human annotators are tasked with labeling the occurrence of very short duration events. We propose a general learning framework for this problem that can accommodate different base classifiers and noise models. We present results on real mobile health data showing that the proposed framework significantly outperforms a number of alternatives including assuming that the label time stamps are noise-free, transforming the problem into the multiple instance learning framework, and learning on labels that were manually re-aligned.