Multi-Time Attention Networks for Irregularly Sampled Time Series
- URL: http://arxiv.org/abs/2101.10318v1
- Date: Mon, 25 Jan 2021 18:57:42 GMT
- Title: Multi-Time Attention Networks for Irregularly Sampled Time Series
- Authors: Satya Narayan Shukla, Benjamin M. Marlin
- Abstract summary: Irregular sampling occurs in many time series modeling applications.
We propose a new deep learning framework for this setting that we call Multi-Time Attention Networks.
Our results show that our approach performs as well or better than a range of baseline and recently proposed models.
- Score: 18.224344440110862
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Irregular sampling occurs in many time series modeling applications where it
presents a significant challenge to standard deep learning models. This work is
motivated by the analysis of physiological time series data in electronic
health records, which are sparse, irregularly sampled, and multivariate. In
this paper, we propose a new deep learning framework for this setting that we
call Multi-Time Attention Networks. 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 our framework on interpolation
and classification tasks using multiple datasets. Our results show that our
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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