  Predicting metrics associated with entities' transnational behavior within
payment processing networks is essential for system monitoring. Multivariate
time series, aggregated from the past transaction history, can provide valuable
insights for such prediction. The general multivariate time series prediction
problem has been well studied and applied across several domains, including
manufacturing, medical, and entomology. However, new domain-related challenges
associated with the data such as concept drift and multi-modality have surfaced
in addition to the real-time requirements of handling the payment transaction
data at scale. In this work, we study the problem of multivariate time series
prediction for estimating transaction metrics associated with entities in the
payment transaction database. We propose a model with five unique components to
estimate the transaction metrics from multi-modality data. Four of these
components capture interaction, temporal, scale, and shape perspectives, and
the fifth component fuses these perspectives together. We also propose a hybrid
offline/online training scheme to address concept drift in the data and fulfill
the real-time requirements. Combining the estimation model with a graphical
user interface, the prototype transaction metric estimation system has
demonstrated its potential benefit as a tool for improving a payment processing
company's system monitoring capability.
