TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes
This addresses the need for interpretable deep learning models in temporal point processes for applications like event prediction, though it appears incremental over existing covariate-TPP approaches.
The paper tackles the problem of incorporating contextual covariates into temporal point process models while maintaining interpretability, proposing TransFeat-TPP which demonstrates improved prediction accuracy and consistent interpretable feature importance compared to existing deep covariate-TPPs.
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.