Classification prediction using attention-based machine learning techniques with temporal sequence data and dynamic co-occurrence graph data objects

Abstract

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a representative embeddings for a plurality of temporal sequences by using a graph attention augmented temporal network based on dynamic co-occurrence graphs for preceding temporal sequences and initial embeddings, where the dynamic co-occurrence graphs are projections of a global guidance co-occurrence graph on classification features of the preceding temporal sequences, and the initial embeddings are generated by processing a latent representation of corresponding classification features that is generated by a sequential long short term memory model as well as a classification feature tree using a tree-based long short term memory model.