Meta-Learning for Neural Network-based Temporal Point Processes (2401.15846v1)

Abstract: Human activities generate various event sequences such as taxi trip records, bike-sharing pick-ups, crime occurrence, and infectious disease transmission. The point process is widely used in many applications to predict such events related to human activities. However, point processes present two problems in predicting events related to human activities. First, recent high-performance point process models require the input of sufficient numbers of events collected over a long period (i.e., long sequences) for training, which are often unavailable in realistic situations. Second, the long-term predictions required in real-world applications are difficult. To tackle these problems, we propose a novel meta-learning approach for periodicity-aware prediction of future events given short sequences. The proposed method first embeds short sequences into hidden representations (i.e., task representations) via recurrent neural networks for creating predictions from short sequences. It then models the intensity of the point process by monotonic neural networks (MNNs), with the input being the task representations. We transfer the prior knowledge learned from related tasks and can improve event prediction given short sequences of target tasks. We design the MNNs to explicitly take temporal periodic patterns into account, contributing to improved long-term prediction performance. Experiments on multiple real-world datasets demonstrate that the proposed method has higher prediction performance than existing alternatives.

Introduction

Temporal point processes (TPP) are a critical framework within the arsenal of predictive modeling techniques, particularly when it comes to human-centric events. They are at the heart of various domain applications, from urban traffic management to public health surveillance. However, TPPs, especially when paired with the precision and flexibility of neural network models, encounter two profound challenges. First, they heavily rely on abundant historical data, which is often scarce, especially for newly initiated systems. Second, real-world applications demand long-term forecasts, which remain a formidable task for most existing TPP models.

Meta-Learning Strategy for TPP

Addressing these issues, a novel meta-learning approach for TPP has been introduced, which aims at enhancing future event prediction capabilities given short sequences of event data. This technique diverges from conventional TPP learning paradigms by leveraging meta-learning—a learning framework that tunes the model's learning algorithm itself based on a variety of tasks, thus allowing rapid adaptation to new, unseen tasks.

The core innovation is twofold: the paper employs a meta-learning framework that first extracts task representations via a recurrent neural network (RNN) from short sequences. Subsequently, these representations serve as inputs to monotonic neural networks (MNNs) for predicting the intensity of events.

The improvement proposed over previous work comes from embedding RNNs within a meta-learning framework without needing gradient-heavy adaptation for new tasks, leading to memory efficiency. Moreover, the inclusion of urban context data, such as land use and community assets, allows the model to cater to unique event patterns driven by spatial factors.

Model Architecture and Experimentation

A detailed architecture is explained, that comprises a task representation encoder and an intensity predictor. The encoder capitalizes on both the temporal information embedded in the short sequences and the contextual urban information, offering a comprehensive task representation. Meanwhile, the intensity predictor is explicitly designed to grasp periodic patterns influencing long-term prediction performance.

In the empirical evaluation performed on diverse real-world datasets, ranging from bike-sharing systems and taxi trip records to crime occurrences, it is demonstrated that the proposed model consistently outperforms a suite of existing methods. Such robust performance is not only indicative of the method's capability to understand the periodicity in data but also its competence in encapsulating multifaceted urban contexts into its predictions.

Conclusion and Future Work

The paper concludes with a compelling showcase of the meta-learning framework's potential to predict future events from scant sequences accurately. While it significantly improves the prediction performance and remains computationally efficient, opening vistas in practically constrained scenarios, it also flags potential improvements. Future iterations may explore alternative sequential models like transformers and integrate richer context forms, including image data, to reinforce predictive capabilities.