Paper Info
Title
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks.
Abstract
A variety of real-world processes (over networks) produce sequences of data whose complex temporal dynamics need to be studied. More especially, the event timestamps can carry important information about the underlying network dynamics, which otherwise are not available from the time-series evenly sampled from continuous signals. Moreover, in most complex processes, event sequences and evenly-sampled times series data can interact with each other, which renders joint modeling of those two sources of data necessary. To tackle the above problems, in this paper, we utilize the rich framework of (temporal) point processes to model event data and timely update its intensity function by the synergic twin Recurrent Neural Networks (RNNs). In the proposed architecture, the intensity function is synergistically modulated by one RNN with asynchronous events as input and another RNN with time series as input. Furthermore, to enhance the interpretability of the model, the attention mechanism for the neural point process is introduced. The whole model with event type and timestamp prediction output layers can be trained end-to-end and allows a black-box treatment for modeling the intensity. We substantiate the superiority of our model in synthetic data and three real-world benchmark datasets.
Year
Venue
Field
2017
arXiv: Learning
Asynchronous communication,Interpretability,Data mining,Network dynamics,Computer science,Point process,Recurrent neural network,Synthetic data,Timestamp,Artificial intelligence,Event sequence,Machine learning
DocType
Volume
Citations 
Journal
abs/1703.08524
1
PageRank 
References 
Authors
0.35
27
6
Name
Order
Citations
PageRank
Shuai Xiao1569.55
Junchi Yan289183.36
Mehrdad Farajtabar323020.70
Le Song42437159.27
Xiaokang Yang53581238.09
Hongyuan Zha66703422.09