The proposed Multi-Time Attention Networks (mTANs) provide a flexible and faster approach to modeling multivariate, sparse, and irregularly sampled time series data compared to existing state-of-the-art methods.
May enhance modeling of irregular cardiovascular time series; leaves open prospective clinical validation.
. Multi-Time Attention Networks learn an embedding of continuous time values and use an attention mechanism to produce a fixed-length representation of a time series containing a variable number of observations. We investigate the performance of this framework on interpolation and classification tasks using multiple datasets. Our results show that the proposed approach performs as well or better than a range of baseline and recently proposed models while offering significantly faster training times than current state-of-the-art methods.
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Shukla et al. (2021) studied this question.
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