论文标题
时间属性通过多关系结构演化的联合建模进行预测
Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
论文作者
论文摘要
时间序列预测是机器学习中的重要问题。时间序列预测的先前方法不涉及其他信息。有了许多动态知识图，我们可以使用此其他信息来更好地预测时间序列。最近，人们关注在动态图上的深度表示学习的应用。这些方法通过对图表中的相互作用进行推理，以预测图形的结构。在本文中，我们提出了一个新框架，以将来自时间序列预测的动态知识图中的信息合并。我们表明，如果图中包含的信息和时间序列数据密切相关，则该相互依赖性可用于以提高精度预测时间序列。我们的框架Dartnet学习了图中每个节点的静态嵌入以及一个动态嵌入，该嵌入取决于动态属性值（时间序列）。然后，它通过采取特定关系均值来捕获社区的信息，并使用RNN编码历史记录信息。我们共同训练模型链接预测和属性预测。我们在五个专门策划的数据集上评估了我们的方法，以解决此问题，并在时间序列预测结果中显示出一致的改进。我们在https://github.com/ink-usc/dartnet上发布了模型Dartnet的数据和代码。
Time series prediction is an important problem in machine learning. Previous methods for time series prediction did not involve additional information. With a lot of dynamic knowledge graphs available, we can use this additional information to predict the time series better. Recently, there has been a focus on the application of deep representation learning on dynamic graphs. These methods predict the structure of the graph by reasoning over the interactions in the graph at previous time steps. In this paper, we propose a new framework to incorporate the information from dynamic knowledge graphs for time series prediction. We show that if the information contained in the graph and the time series data are closely related, then this inter-dependence can be used to predict the time series with improved accuracy. Our framework, DArtNet, learns a static embedding for every node in the graph as well as a dynamic embedding which is dependent on the dynamic attribute value (time-series). Then it captures the information from the neighborhood by taking a relation specific mean and encodes the history information using RNN. We jointly train the model link prediction and attribute prediction. We evaluate our method on five specially curated datasets for this problem and show a consistent improvement in time series prediction results. We release the data and code of model DArtNet for future research at https://github.com/INK-USC/DArtNet .
