Flexible and Efficient Multivariate Temporal Point Process Learning via Cumulative Hazard Function Modeling

Khái niệm cốt lõi

The proposed model efficiently models the multivariate temporal point process by directly learning a flexible but well-defined cumulative hazard function, enabling accurate and efficient likelihood evaluation.

Tóm tắt

The paper presents a flexible and well-defined cumulative hazard function (CHF) network and incorporates it into a parameter-efficient multivariate temporal point process (MTPP) model.
Key highlights:

Most existing MTPP models define intensity (or density) functions for each event type, which can be prohibitively expensive when dealing with real-world event sequences with numerous event types.
The proposed model directly models the CHF and the time-dependent type distribution, significantly reducing the model complexity compared to existing methods.
The CHF network is designed to satisfy the mathematical constraints of a valid CHF, addressing the limitations of previous CHF-based methods.
Experimental results on six datasets show that the proposed model achieves state-of-the-art performance on data fitting and event prediction tasks, while having significantly fewer parameters and memory usage than strong competitors.
The paper first introduces the background on temporal point processes and neural point process models. It then presents the details of the proposed model architecture, including the CHF network, the time predictor, and the time-dependent type predictor. The training loss function is also described.
The experimental evaluation compares the proposed model against various state-of-the-art baselines on data fitting, event type prediction, and event time prediction tasks. The results demonstrate the effectiveness of the proposed approach, outperforming the baselines on most datasets. Additionally, the paper analyzes the model complexity in terms of required parameters and memory usage, showing significant reductions compared to the competitors.
The paper also includes ablation studies to investigate the impact of the hyperparameter α in the loss function and the choice of activation function in the CHF network.

Thống kê

The proposed model achieves the lowest negative log-likelihood (per sequence) on 5 out of 6 datasets compared to the baselines.
The proposed model has the lowest mean absolute error for event time prediction on most datasets.

Trích dẫn

"Most existing MTPP models define intensity (or density) functions for each event type, which can be prohibitively expensive when dealing with real-world event sequences with numerous event types."
"The proposed model directly models the CHF and the time-dependent type distribution, significantly reducing the model complexity compared to existing methods."
"Experimental results on six datasets show that the proposed model achieves state-of-the-art performance on data fitting and event prediction tasks, while having significantly fewer parameters and memory usage than strong competitors."

Thông tin chi tiết chính được chắt lọc từ

Cumulative Hazard Function Based Efficient Multivariate Temporal Point Process Learning

by Bingqing Liu lúc arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13663.pdf

Yêu cầu sâu hơn

How can the proposed model be extended to handle event sequences with evolving event types over time

To handle event sequences with evolving event types over time, the proposed model can be extended by incorporating a mechanism to capture the dynamics of changing event types. One approach could be to introduce a time-varying component in the type distribution predictor. By allowing the type distribution to evolve over time, the model can adapt to shifts in event types and better capture the changing patterns in the data. Additionally, incorporating a mechanism for detecting and adapting to changes in event types, such as using attention mechanisms or recurrent structures, can enhance the model's ability to handle evolving event sequences.

What are the potential limitations of the CHF-based approach, and how can they be addressed in future research

The CHF-based approach, while promising for accurate likelihood evaluation in temporal point process modeling, has some potential limitations that need to be addressed in future research. One limitation is the assumption of monotonicity in the CHF network, which may restrict the flexibility of the model. Future research could explore more sophisticated architectures or activation functions that can capture complex temporal dynamics without sacrificing monotonicity. Additionally, the reliance on neural networks for modeling the CHF may introduce challenges related to interpretability and generalizability. Future work could focus on developing methods to enhance the interpretability of the CHF-based models and ensure robust performance across diverse datasets.

How can the proposed model be applied to other domains beyond event sequence modeling, such as time series forecasting or survival analysis

The proposed model can be applied to other domains beyond event sequence modeling, such as time series forecasting or survival analysis, by adapting the architecture and training objectives to suit the specific requirements of these domains. For time series forecasting, the model can be modified to predict future time points and event types based on historical data, enabling accurate predictions of future events. In survival analysis, the model can be tailored to predict the likelihood of an event occurring at a given time, taking into account the evolving nature of event types. By customizing the model architecture and loss functions, the proposed approach can be effectively applied to a wide range of time-dependent prediction tasks in various domains.