GRU-M: A Joint Impute and Learn Approach for Sequential Prediction under Missing Data

Soumen Pachal, Avinash Achar, Nancy Bhutani, Akash Das

Keywords: Sequential Prediction, Missing Data, RNN, GRU, Time-series

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TL;DR: The paper introduces an RNN unit which is an extension of GRU to intelligently tackle missing data during sequence (time-series) prediction.

Abstract:

Sequential Prediction in presence of missing data is an old research problem. Classically,
researchers have tackled this by imputing data first and then building predictive models.
This 2-stage process is typically prone to errors and to circumvent this, researchers have
provided a variety of techniques which employ a joint impute and learn approach before
prediction. Among these, Recurrent Neural Networks (RNNs) have been very popular given
their natural ability to tackle sequential data efficiently. Existing state-of-art approaches
either (i)do not impute (ii) do not completely factor the available information around a gap,
(iii)ignore position information within a gap and so on. Our approach intelligently addresses
these gaps by proposing a novel architecture which jointly imputes and learns by taking
into account (i)information from either end of the gap (ii)proximity to the left/right-end of
a gap (iii)the length of the gap. In context of this work, prediction means either sequence
classification or forecasting. In this paper, we demonstrate the utility of the proposed
architecture on forecasting tasks. We benchmark against a range of state-of-art baselines
and in scenarios where data is either (a)naturally missing or (b)synthetically masked.

Primary Area: Deep Learning (architectures, deep reinforcement learning, generative models, deep learning theory, etc.)

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Student Author: No

Submission Number: 243