Time-series forecasting is one of the most active research topics in artificial intelligence. It has the power to bring light to problems in several areas of knowledge, such as epidemiological studies, healthcare inference, and climate change analysis. Applications in real-world time series should consider two factors for achieving reliable predictions: modeling dynamic dependencies among multiple variables and adjusting the model's intrinsic hyperparameters. An open gap in the literature is that statistical and ensemble learning approaches systematically present lower predictive performance than deep learning methods. The existing applications consistently 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, named after 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 tens of ensemble methods and classical statistical ones. The results outperformed both statistical and ensemble-learning approaches, showing an improvement of 64.87 percent over the competing algorithms on the SARS-CoV-2 dataset of the renowned John Hopkins University for 188 countries simultaneously. For further validation, we tested our architecture in two other public datasets of different domains, the PhysioNet Computing in Cardiology Challenge 2012 and Brazilian Weather datasets. We also analyzed the Evolution Weights arising from the hidden layers of ReGENN to describe how the variables of the dataset interact with each other; and, as a result of looking at inter and intra-temporal relationships simultaneously, we concluded that time-series forecasting is majorly improved if paying attention to how multiple multivariate data synchronously evolve.

中文翻译：

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关注进化：利用深度图进化学习进行时间序列预测


时间序列预测是人工智能领域最活跃的研究课题之一。它有能力揭示多个知识领域的问题，例如流行病学研究、医疗保健推断和气候变化分析。现实世界时间序列中的应用程序应考虑实现可靠预测的两个因素：对多个变量之间的动态依赖性进行建模以及调整模型的内在超参数。文献中的一个空白是统计和集成学习方法系统地表现出比深度学习方法更低的预测性能。现有的应用程序始终忽视与多个时间序列表示的多元数据纠缠在一起的数据序列方面。相反，这项工作提出了一种用于时间序列预测的新颖神经网络架构，它将图进化的力量与对不同数据分布的深度循环学习相结合，以循环图进化神经网络（ReGENN）命名。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关系（即来自自身的观察），还取决于外部变量和时间间关系，来推断同时发生的时间序列之间的多个多元关系（即来自其他自我的观察）。我们进行了大量实验，将 ReGENN 与数十种集成方法和经典统计方法进行了比较。结果优于统计和集成学习方法，在著名的约翰·霍普金斯大学的 188 个国家的 SARS-CoV-2 数据集上同时显示比竞争算法提高了 64.87%。 为了进一步验证，我们在不同领域的另外两个公共数据集（2012 年心脏病学挑战赛中的 PhysioNet 计算和巴西天气数据集）中测试了我们的架构。我们还分析了 ReGENN 隐藏层产生的进化权重，以描述数据集的变量如何相互作用；而且，通过同时观察时间间和时间内关系，我们得出结论，如果关注多个多元数据如何同步演化，时间序列预测将得到显着改善。