论文标题
商户交易预测的多流RNN
Multi-stream RNN for Merchant Transaction Prediction
论文作者
论文摘要
最近，数字支付系统已大大改变了人们的生活方式。在监视和保证付款处理系统的完整性方面已经出现了新的挑战。一项重要的任务是预测每个商人的未来交易统计。因此，这些预测可用于指导其他任务，从欺诈检测到建议。这个问题具有挑战性，因为我们不仅需要预测多元时间序列，还需要预测未来的多步骤。在这项工作中，我们为根据这些要求量身定制的多步商人交易预测提出了一个多流RNN模型。拟议的多流RNN以不同的粒度总结了交易数据，并对将来多个步骤进行了预测。我们广泛的实验结果表明，所提出的模型能够超过现有的最新方法。
Recently, digital payment systems have significantly changed people's lifestyles. New challenges have surfaced in monitoring and guaranteeing the integrity of payment processing systems. One important task is to predict the future transaction statistics of each merchant. These predictions can thus be used to steer other tasks, ranging from fraud detection to recommendation. This problem is challenging as we need to predict not only multivariate time series but also multi-steps into the future. In this work, we propose a multi-stream RNN model for multi-step merchant transaction predictions tailored to these requirements. The proposed multi-stream RNN summarizes transaction data in different granularity and makes predictions for multiple steps in the future. Our extensive experimental results have demonstrated that the proposed model is capable of outperforming existing state-of-the-art methods.
