A Cluster-Oriented Bayesian Network Approach for Mixed-Type Event Prediction With Application in Order Logistics

Abstract

Temporal representation and reasoning for probabilistic events describe temporal causal relationships between events. This has been widely used in several applications to predict events accurately. However, there are two challenges: the occurrence time points of events may have distinct types, distributed randomly or at several fixed time points; only limited historical data are available in some cases. This article presents a mixed-type event prediction algorithm based on a cluster-oriented Bayesian network (BN) model to address the highlighted challenges. The proposed model categorizes events as random events or timing events based on their temporal features. The similarity between events is measured according to event types and features. A clustering algorithm for events is further implemented to help reduce the model size and build a simpler and more accurate BN. The experimental results show that the proposed model significantly improves performance under small data sizes.