  Time series forecasting is essential for decision making in many domains. In
this work, we address the challenge of predicting prices evolution among
multiple potentially interacting financial assets. A solution to this problem
has obvious importance for governments, banks, and investors. Statistical
methods such as Auto Regressive Integrated Moving Average (ARIMA) are widely
applied to these problems. In this paper, we propose to approach economic time
series forecasting of multiple financial assets in a novel way via video
prediction. Given past prices of multiple potentially interacting financial
assets, we aim to predict the prices evolution in the future. Instead of
treating the snapshot of prices at each time point as a vector, we spatially
layout these prices in 2D as an image, such that we can harness the power of
CNNs in learning a latent representation for these financial assets. Thus, the
history of these prices becomes a sequence of images, and our goal becomes
predicting future images. We build on a state-of-the-art video prediction
method for forecasting future images. Our experiments involve the prediction
task of the price evolution of nine financial assets traded in U.S. stock
markets. The proposed method outperforms baselines including ARIMA, Prophet,
and variations of the proposed method, demonstrating the benefits of harnessing
the power of CNNs in the problem of economic time series forecasting.
