ITPP: Learning Disentangled Event Dynamics in Marked Temporal Point Processes

• URL: http://arxiv.org/abs/2511.06032v1
• Date: Sat, 08 Nov 2025 15:00:25 GMT
• Title: ITPP: Learning Disentangled Event Dynamics in Marked Temporal Point Processes
• Authors: Wang-Tao Zhou, Zhao Kang, Ke Yan, Ling Tian,
• Abstract summary: Marked Temporal Point Processes (MTPPs) provide a principled framework for modeling asynchronous event sequences by conditioning on the history of past events.<n>Most existing MTPP models rely on channel-mixing strategies that encode information from different event types into a single, fixed-size latent representation.<n>In this work, we introduce ITPP, a novel channel-independent architecture for MTPP modeling that decouples event type information using an encoder-decoder framework with an ODE-based backbone.
• Score: 15.224545514789819
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Marked Temporal Point Processes (MTPPs) provide a principled framework for modeling asynchronous event sequences by conditioning on the history of past events. However, most existing MTPP models rely on channel-mixing strategies that encode information from different event types into a single, fixed-size latent representation. This entanglement can obscure type-specific dynamics, leading to performance degradation and increased risk of overfitting. In this work, we introduce ITPP, a novel channel-independent architecture for MTPP modeling that decouples event type information using an encoder-decoder framework with an ODE-based backbone. Central to ITPP is a type-aware inverted self-attention mechanism, designed to explicitly model inter-channel correlations among heterogeneous event types. This architecture enhances effectiveness and robustness while reducing overfitting. Comprehensive experiments on multiple real-world and synthetic datasets demonstrate that ITPP consistently outperforms state-of-the-art MTPP models in both predictive accuracy and generalization.

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