Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
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DOI：
10.24963/ijcai.2020/386
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Sankalp Garg;Navodita Sharma;Woojeong Jin;Xiang Ren
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文献类型：
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作者：
Sankalp Garg;Navodita Sharma;Woojeong Jin;Xiang Ren
时间序列预测是机器学习中的一个重要问题。以前的时间序列预测方法不涉及额外的信息。有了大量可用的动态知识图，我们可以使用这些附加信息来更好地预测时间序列。最近，深度表示学习在动态图上的应用成为人们关注的焦点。这些方法通过推理先前时间步骤中图中的交互来预测图的结构。在本文中，我们提出了一个新的框架来合并动态知识图的信息以进行时间序列预测。我们证明，如果图表中包含的信息和时间序列数据密切相关，那么这种相互依赖性可以用来提高预测时间序列的准确性。我们的框架 DArtNet 学习图中每个节点的静态嵌入以及依赖于动态属性值（时间序列）的动态嵌入。然后，它通过采用关系特定均值来捕获来自邻域的信息，并使用 RNN 对历史信息进行编码。我们联合训练模型链接预测和属性预测。我们针对这个问题在五个专门策划的数据集上评估了我们的方法，并显示出时间序列预测结果的持续改进。我们发布了模型 DArtNet 的数据和代码以供将来的研究。
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.