Time-series forecasting is one of the most active research topics in artificial intelligence. A still open gap in that literature is that statistical and ensemble learning approaches systematically present lower predictive performance than deep learning methods. They generally disregard the data sequence aspect entangled with multivariate data represented in more than one time series. Conversely, this work presents a novel neural network architecture for time-series forecasting that combines the power of graph evolution with deep recurrent learning on distinct data distributions; we named our method Recurrent Graph Evolution Neural Network (ReGENN). The idea is to infer multiple multivariate relationships between co-occurring time-series by assuming that the temporal data depends not only on inner variables and intra-temporal relationships (i.e., observations from itself) but also on outer variables and inter-temporal relationships (i.e., observations from other-selves). An extensive set of experiments was conducted comparing ReGENN with dozens of ensemble methods and classical statistical ones, showing sound improvement of up to 64.87% over the competing algorithms. Furthermore, we present an analysis of the intermediate weights arising from ReGENN, showing that by looking at inter and intra-temporal relationships simultaneously, time-series forecasting is majorly improved if paying attention to how multiple multivariate data synchronously evolve.

中文翻译：

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注意演化：利用深度图演化学习进行时间序列预测。

时间序列预测是人工智能中最活跃的研究主题之一。该文献中仍然存在的一个空白是统计和整体学习方法系统地表现出比深度学习方法低的预测性能。他们通常不理会与在多个时间序列中表示的多元数据纠缠的数据序列方面。相反，这项工作提出了一种用于时序预测的新型神经网络体系结构，该体系结构将图进化的能力与对不同数据分布的深度循环学习相结合。我们将我们的方法命名为递归图演化神经网络（ReGENN）。这个想法是通过假设时间数据不仅取决于内部变量和时间内部关系（即，本身的观察），也包括外部变量和时间间关系（即来自其他自我的观察）。进行了一系列广泛的实验，将ReGENN与数十种集成方法和经典统计方法进行了比较，显示出与竞争算法相比，声音的改进高达64.87％。此外，我们对ReGENN产生的中间权重进行了分析，结果表明，通过同时查看时间间和时间间关系，如果关注多个多元数据如何同步演化，则可以大大改善时间序列预测。进行了广泛的实验，将ReGENN与数十种集成方法和经典统计方法进行了比较，显示出与竞争算法相比，声音的改进高达64.87％。此外，我们对ReGENN产生的中间权重进行了分析，结果表明，通过同时查看时间间和时间间关系，如果关注多个多元数据如何同步演化，则可以大大改善时间序列预测。进行了广泛的实验，将ReGENN与数十种集成方法和经典统计方法进行了比较，显示出与竞争算法相比，声音的改进高达64.87％。此外，我们对ReGENN产生的中间权重进行了分析，结果表明，通过同时查看时间间和时间间关系，如果关注多个多元数据如何同步演化，则可以大大改善时间序列预测。