EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables. (arXiv:2208.01913v1 [cs.LG])

While exogenous variables have a major impact on performance improvement in
time series analysis, inter-series correlation and time dependence among them
are rarely considered in the present continuous methods. The dynamical systems
of multivariate time series could be modelled with complex unknown partial
differential equations (PDEs) which play a prominent role in many disciplines
of science and engineering. In this paper, we propose a continuous-time model
for arbitrary-step prediction to learn an unknown PDE system in multivariate
time series whose …

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