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.

LSTM 网络在时间序列预测任务中表现出色，但是在集成未来、依赖时间的协变量方面的结构不够灵活。本研究通过引入高度相关的协变量到未来时间步的人工时间序列数据集中，评估了 LSTM 网络在考虑这些协变量时的性能，并将其与单变量基线进行了比较。研究结果显示，联合训练协变量和目标变量可以提高模型的整体性能，但在多变量和单变量预测之间存在显著的性能差异。这些结果表明，LSTM 结构可能不适用于那些协变量预测通常会提升模型准确性的预测任务。