Human behavior drives a range of complex social, urban, and economic systems,
yet understanding its structure and dynamics at the individual level remains an
open question. From credit card transactions to communications data, human
behavior appears to exhibit bursts of activity driven by task prioritization
and periodicity, however, current research does not offer generative models
capturing these mechanisms. We propose a multivariate, periodic Hawkes process
(MPHP) model that captures -- at the individual level -- the temporal
clustering of human activity, the interdependence structure and co-excitation
of different activities, and the periodic effects of weekly rhythms. We also
propose a scalable parameter estimation technique for this model using
maximum-aposteriori expectation-maximization that additionally provides
estimation of latent variables revealing branching structure of an individual's
behavior patterns. We apply the model to a large dataset of credit card
transactions, and demonstrate the MPHP outperforms a non-homogeneous Poisson
model and LDA in both statistical fit for the distribution of inter-event times
and an activity prediction task.

通过提出一种作为生成模型的多变量周期 Hawkes 过程模型，该模型可以捕捉人类活动的时间聚类、相互依赖结构和相互兴奋以及一周内时间周期效应。我们将这个模型应用于大规模信用卡交易数据集，并证明 MPHP 在交互事件时间分布和活动预测任务的统计适合度上超过非齐次泊松模型和 LDA。

利用数字足迹对人类动态和生活方式建模

Modeling Human Dynamics and Lifestyle Using Digital Traces

The expansion of the electronic commerce, together with an increasing
confidence of customers in electronic payments, makes of fraud detection a
critical factor. Detecting frauds in (nearly) real time setting demands the
design and the implementation of scalable learning techniques able to ingest
and analyse massive amounts of streaming data. Recent advances in analytics and
the availability of open source solutions for Big Data storage and processing
open new perspectives to the fraud detection field. In this paper we present a
SCAlable Real-time Fraud Finder (SCARFF) which integrates Big Data tools
(Kafka, Spark and Cassandra) with a machine learning approach which deals with
imbalance, nonstationarity and feedback latency. Experimental results on a
massive dataset of real credit card transactions show that this framework is
scalable, efficient and accurate over a big stream of transactions.

本文介绍了一种利用大数据工具（Kafka、Spark 和 Cassandra）和机器学习方法集成处理欺诈检测的框架 SCARFF。在真实的信用卡交易大数据集上进行实验，结果表明该框架能够有效地处理交易流，并且具有高扩展性和准确性。