RoTHP: Rotary Position Embedding-based Transformer Hawkes Process

TL;DR

This paper introduces RoTHP, a novel Transformer Hawkes Process using rotary position embeddings, which improves sequence prediction flexibility and robustness to temporal noise in modeling asynchronous event data.

Contribution

The paper proposes a rotary position embedding-based architecture for Transformer Hawkes Processes, enhancing sequence prediction and robustness to temporal variations.

Findings

RoTHP exhibits better generalization to timestamp translations.

RoTHP demonstrates improved sequence prediction accuracy.

Theoretical analysis confirms translation invariance and flexibility.

Abstract

Temporal Point Processes (TPPs), especially Hawkes Process are commonly used for modeling asynchronous event sequences data such as financial transactions and user behaviors in social networks. Due to the strong fitting ability of neural networks, various neural Temporal Point Processes are proposed, among which the Neural Hawkes Processes based on self-attention such as Transformer Hawkes Process (THP) achieve distinct performance improvement. Although the THP has gained increasing studies, it still suffers from the {sequence prediction issue}, i.e., training on history sequences and inferencing about the future, which is a prevalent paradigm in realistic sequence analysis tasks. What's more, conventional THP and its variants simply adopt initial sinusoid embedding in transformers, which shows performance sensitivity to temporal change or noise in sequence data analysis by our empirical study. To deal with the problems, we propose a new Rotary Position Embedding-based THP (RoTHP) architecture in this paper. Notably, we show the translation invariance property and {sequence prediction flexibility} of our RoTHP induced by the {relative time embeddings} when coupled with Hawkes process theoretically. Furthermore, we demonstrate empirically that our RoTHP can be better generalized in sequence data scenarios with timestamp translations and in sequence prediction tasks.