  Autoregressive Recurrent Neural Networks are widely employed in time-series
forecasting tasks, demonstrating effectiveness in univariate and certain
multivariate scenarios. However, their inherent structure does not readily
accommodate the integration of future, time-dependent covariates. A proposed
solution, outlined by Salinas et al 2019, suggests forecasting both covariates
and the target variable in a multivariate framework. In this study, we
conducted comprehensive tests on publicly available time-series datasets,
artificially introducing highly correlated covariates to future time-step
values. Our evaluation aimed to assess the performance of an LSTM network when
considering these covariates and compare it against a univariate baseline. As
part of this study we introduce a novel approach using seasonal time segments
in combination with an RNN architecture, which is both simple and extremely
effective over long forecast horizons with comparable performance to many state
of the art architectures. Our findings from the results of more than 120 models
reveal that under certain conditions jointly training covariates with target
variables can improve overall performance of the model, but often there exists
a significant performance disparity between multivariate and univariate
predictions. Surprisingly, even when provided with covariates informing the
network about future target values, multivariate predictions exhibited inferior
performance. In essence, compelling the network to predict multiple values can
prove detrimental to model performance, even in the presence of informative
covariates. These results suggest that LSTM architectures may not be suitable
for forecasting tasks where predicting covariates would typically be expected
to enhance model accuracy.
