多未来商家交易预测
Multi-future Merchant Transaction Prediction
摘要：
从商家交易历史生成的多变量时间序列可以为支付处理公司提供关键的见解。预测商家未来的能力对于欺诈检测和推荐系统至关重要。传统上，这个问题是制定预测一个多变量的时间序列下的多地平线设置。然而，现实世界的应用往往需要一个以上的未来趋势预测考虑的不确定性，其中多个多变量的时间序列需要预测。这个问题被称为多未来预测。本文将这两个研究方向结合起来，提出了研究多未来、多时域、多变量时间序列预测这一新问题。这个问题是至关重要的，因为它在金融行业有广泛的用例，可以通过提供替代未来来降低风险，同时改善用户体验。这个问题也很有挑战性，因为现在我们不仅需要捕捉过去的模式和见解，还需要训练一个具有强大推理能力的模型，以预测多种可能的结果。为了解决这个问题，我们提出了一个新的模型，使用卷积神经网络和一个简单而有效的编码器-解码器结构，从多个角度学习的时间序列模式。我们使用真实世界的商人交易数据的实验来证明我们提出的模型的有效性。我们还在实验部分对不同的模型设计选择进行了广泛的讨论。
英文摘要：
The multivariate time series generated from merchant transaction history can provide critical insights for payment processing companies. The capability of predicting merchants' future is crucial for fraud detection and recommendation systems. Conventionally, this problem is formulated to predict one multivariate time series under the multi-horizon setting. However, real-world applications often require more than one future trend prediction considering the uncertainties, where more than one multivariate time series needs to be predicted. This problem is called multi-future prediction. In this work, we combine the two research directions and propose to study this new problem: multi-future, multi-horizon and multivariate time series prediction. This problem is crucial as it has broad use cases in the financial industry to reduce the risk while improving user experience by providing alternative futures. This problem is also challenging as now we not only need to capture the patterns and insights from the past but also train a model that has a strong inference capability to project multiple possible outcomes. To solve this problem, we propose a new model using convolutional neural networks and a simple yet effective encoder-decoder structure to learn the time series pattern from multiple perspectives. We use experiments on real-world merchant transaction data to demonstrate the effectiveness of our proposed model. We also provide extensive discussions on different model design choices in our experimental section.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
