  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.
