Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks (1705.08982v1)

Abstract: Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series while the other need to handle the history events. In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics. The whole model with event type and timestamp prediction output layers can be trained end-to-end. Our approach takes an RNN perspective to point process, and models its background and history effect. For utility, our method allows a black-box treatment for modeling the intensity which is often a pre-defined parametric form in point processes. Meanwhile end-to-end training opens the venue for reusing existing rich techniques in deep network for point process modeling. We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.

• The paper proposes a dual-RNN framework that effectively captures both background and historical event influences in point processes.
• The innovative architecture outperforms baseline models by accurately predicting complex event sequences and failure types in ATM datasets.
• The approach opens avenues for further research into non-parametric RNN models applicable across domains like social media, conflict resolution, and healthcare.

An Analysis of Modeling The Intensity Function of Point Process Via Recurrent Neural Networks

The paper "Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks" by Shuai Xiao and colleagues introduces a novel approach to modeling point processes using Recurrent Neural Networks (RNNs). The work is significant in the context of leveraging neural networks for capturing the dynamics of event sequences where timestamps play a crucial role.

The core proposition of this paper is the use of RNNs to model both the background and historical components inherent in the intensity functions of point processes. Traditional parametric forms of point process models often fail to effectively capture the richness and variability of real-world event data, leading to issues such as underfitting or mis-specification. This research circumvents these limitations by employing a non-parametric approach, thereby offering a broader and more flexible modeling framework.

The architecture proposed in this paper employs two distinct RNNs: one aligned with time series indexed events and the other with asynchronous events. This dual-RNN framework is designed to capture both the spontaneous background processes and the long-reaching effects of historical events. This innovative setup allows the model to handle complex dynamics that static parametrical methods might overlook. These RNNs facilitate an end-to-end learning process, amalgamating advantages from deep learning techniques while modeling point processes.

An application of the model is demonstrated through a predictive maintenance task involving a dataset of Automated Teller Machines (ATMs). The model is tasked with predicting failure types and associated timestamps from event logs. This application underscores the practical utility of the proposed method, particularly in industries where predictive maintenance can preemptively address operational risks and optimize resource allocation.

From a quantitative standpoint, the model's ability to predict event types and timestamps was systematically evaluated against baseline models like the Logistic Model, RMTPP, and Hawkes Process. Results indicated superior performance of the proposed dual-RNN approach, especially for complex subtype predictions and timing accuracy. The empirical findings support the model's enhanced capability in handling intricate temporal patterns without requiring domain-specific assumptions.

This work opens several avenues for future exploration. The integration of RNN-based methods with point processes could be further refined by exploring variational methods or incorporating attention mechanisms to potentially improve prediction accuracies. Moreover, expanding the model's application to other domains such as social media dynamics, conflict resolution, and healthcare could validate its generalizability and robustness.

In summary, this paper contributes a significant advancement in the application of deep learning techniques to point process modeling. It sets a precedent for future research efforts on leveraging non-parametric models to capture complex temporal event dynamics, thereby enhancing predictive capabilities in various domains.