用于商家交易预测的多流RNN
Multi-stream RNN for Merchant Transaction Prediction
摘要：
近年来，数字支付系统显著改变了人们的生活方式。在监测和保证支付处理系统的完整性方面出现了新的挑战。一个重要的任务是预测每个商家未来的交易统计数据。因此，这些预测可以用于指导其他任务，从欺诈检测到推荐。这个问题是具有挑战性的，因为我们不仅需要预测多变量时间序列，但也多步到未来。在这项工作中，我们提出了一个多流RNN模型，用于针对这些需求量身定制的多步商家交易预测。所提出的多流RNN以不同的粒度总结交易数据，并对未来的多个步骤进行预测。我们广泛的实验结果表明，所提出的模型是能够优于现有的国家的最先进的方法。
英文摘要：
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.
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