Modelling Irregularly Sampled Time Series Without Imputation
Rohit Agarwal Aman Sinha Dilip K. Prasad Marianne Clausel Alexander Horsch Mathieu Constant Xavier Coubez
Modelling irregularly-sampled time series (ISTS) is challenging because of
missing values. Most existing methods focus on handling ISTS by converting
irregularly sampled data into regularly sampled data via imputation. These
models assume an underlying missing mechanism leading to unwanted bias and
sub-optimal performance. We present SLAN (Switch LSTM Aggregate Network), which
utilizes a pack of LSTMs to model ISTS without imputation, eliminating the
assumption of any underlying process. It dynamically adapts its architecture on
the fly based on the measured sensors. SLAN exploits the irregularity
information to capture each sensor's local summary explicitly and maintains a
global summary state throughout the observational period. We demonstrate the
efficacy of SLAN on publicly available datasets, namely, MIMIC-III, Physionet
2012 and Physionet 2019. The code is available at
https://github.com/Rohit102497/SLAN.