  Irregular multivariate time series data is characterized by varying time
intervals between consecutive observations of measured variables/signals (i.e.,
features) and varying sampling rates (i.e., recordings/measurement) across
these features. Modeling time series while taking into account these
irregularities is still a challenging task for machine learning methods. Here,
we introduce TADA, a Two-stageAggregation process with Dynamic local Attention
to harmonize time-wise and feature-wise irregularities in multivariate time
series. In the first stage, the irregular time series undergoes temporal
embedding (TE) using all available features at each time step. This process
preserves the contribution of each available feature and generates a
fixed-dimensional representation per time step. The second stage introduces a
dynamic local attention (DLA) mechanism with adaptive window sizes. DLA
aggregates time recordings using feature-specific windows to harmonize
irregular time intervals capturing feature-specific sampling rates. Then
hierarchical MLP mixer layers process the output of DLA through multiscale
patching to leverage information at various scales for the downstream tasks.
TADA outperforms state-of-the-art methods on three real-world datasets,
including the latest MIMIC IV dataset, and highlights its effectiveness in
handling irregular multivariate time series and its potential for various
real-world applications.
