Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks

Understand

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).