A point process model for rare event detection

TL;DR

This paper introduces a Hawkes process-based model for detecting rare events, leveraging temporal dynamics to improve prediction accuracy over traditional machine learning classifiers.

Contribution

It develops a novel classification framework using Hawkes processes that captures temporal dependencies, enhancing rare event detection performance.

Findings

Improved predictive accuracy over conventional classifiers

Effective modeling of temporal event dependencies

Insights into the temporal dynamics of e-commerce transactions

Abstract

Detecting rare events, those defined to give rise to high impact but have a low probability of occurring, is a challenge in a number of domains including meteorological, environmental, financial and economic. The use of machine learning to detect such events is becoming increasingly popular, since they offer an effective and scalable solution when compared to traditional signature-based detection methods. In this work, we begin by undertaking exploratory data analysis, and present techniques that can be used in a framework for employing machine learning methods for rare event detection. Strategies to deal with the imbalance of classes including the selection of performance metrics are also discussed. Despite their popularity, we believe the performance of conventional machine learning classifiers could be further improved, since they are agnostic to the natural order over time in which the events occur. Stochastic processes on the other hand, model sequences of events by exploiting their temporal structure such as clustering and dependence between the different types of events. We develop a model for classification based on Hawkes processes and apply it to a dataset of e-commerce transactions, resulting in not only better predictive performance but also deriving inferences regarding the temporal dynamics of the data.