TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes

TL;DR

This paper introduces TransFeat-TPP, a Transformer-based model that enhances interpretability and accuracy in deep covariate temporal point processes by effectively modeling event-covariate relationships.

Contribution

It proposes a novel Transformer-based framework that improves interpretability and predictive performance in covariate-informed temporal point processes.

Findings

Improved prediction accuracy on synthetic and real datasets.

Enhanced interpretability of covariate importance.

Consistent identification of relevant covariates.

Abstract

The classical temporal point process (TPP) constructs an intensity function by taking the occurrence times into account. Nevertheless, occurrence time may not be the only relevant factor, other contextual data, termed covariates, may also impact the event evolution. Incorporating such covariates into the model is beneficial, while distinguishing their relevance to the event dynamics is of great practical significance. In this work, we propose a Transformer-based covariate temporal point process (TransFeat-TPP) model to improve the interpretability of deep covariate-TPPs while maintaining powerful expressiveness. TransFeat-TPP can effectively model complex relationships between events and covariates, and provide enhanced interpretability by discerning the importance of various covariates. Experimental results on synthetic and real datasets demonstrate improved prediction accuracy and consistently interpretable feature importance when compared to existing deep covariate-TPPs.