Concurrent Multi-Label Prediction in Event Streams

Xiao Shou, Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Kristin Bennett

Abstract:

Streams of irregularly occurring events are commonly modeled as a marked temporal point process. Many real-world
datasets such as e-commerce transactions and electronic
health records often involve events where multiple event
types co-occur, e.g. multiple items purchased or multiple
diseases diagnosed simultaneously. In this paper, we tackle
multi-label prediction in such a problem setting, and propose
a novel Transformer-based Conditional Mixture of Bernoulli
Network (TCMBN) that leverages neural density estimation
to capture complex temporal dependence as well as probabilistic dependence between concurrent event types. We also
propose potentially incorporating domain knowledge in the
objective by regularizing the predicted probability. To represent probabilistic dependence of concurrent event types
graphically, we design a two-step approach that first learns
the mixture of Bernoulli network and then solves a least squares semi-definite constrained program to numerically approximate the sparse precision matrix from a learned covariance matrix. This approach proves to be effective for event
prediction while also providing an interpretable and possibly
non-stationary structure for insights into event co-occurrence.
We demonstrate the superior performance of our approach
compared to existing baselines on multiple synthetic and real
benchmarks.