  In multivariate time series (MTS) forecasting, existing state-of-the-art deep
learning approaches tend to focus on autoregressive formulations and overlook
the information within exogenous indicators. To address this limitation, we
present DeformTime, a neural network architecture that attempts to capture
correlated temporal patterns from the input space, and hence, improve
forecasting accuracy. It deploys two core operations performed by deformable
attention blocks (DABs): learning dependencies across variables from different
time steps (variable DAB), and preserving temporal dependencies in data from
previous time steps (temporal DAB). Input data transformation is explicitly
designed to enhance learning from the deformed series of information while
passing through a DAB. We conduct extensive experiments on 6 MTS data sets,
using previously established benchmarks as well as challenging infectious
disease modelling tasks with more exogenous variables. The results demonstrate
that DeformTime improves accuracy against previous competitive methods across
the vast majority of MTS forecasting tasks, reducing the mean absolute error by
10% on average. Notably, performance gains remain consistent across longer
forecasting horizons.
