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.

翻译：自回归递归神经网络广泛应用于时间序列预测任务，在单变量和某些多变量场景中展现出有效性。然而，其固有结构难以直接整合未来的时间相关协变量。Salinas等人（2019）提出了一种解决方案，建议在多变量框架下同时预测协变量和目标变量。本研究对公开可用的时间序列数据集进行了全面测试，人为引入与未来时间步值高度相关的协变量。我们的评估旨在检验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比较。作为研究的一部分，我们提出了一种结合季节性时间分段与RNN架构的新方法，该方法既简单又在长预测区间上极为有效，且性能可与许多先进架构相媲美。基于120多个模型的结果表明，在特定条件下将协变量与目标变量联合训练可改善模型整体性能，但多变量预测与单变量预测之间往往存在显著性能差距。令人惊讶的是，即使向网络提供告知未来目标值的协变量，多变量预测仍表现出更差性能。本质上，迫使网络预测多个值可能会损害模型性能，即使在存在信息性协变量的情况下也是如此。这些结果表明，LSTM架构可能不适合那些通常期望通过预测协变量来提高模型准确性的预测任务。