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 this https URL .

本文提出了一個新的框架 DArtNet，它利用了動態知識圖中的信息進行時間序列預測，經實驗證明可以提高預測準確率。