Transforming Finance Into Vision: Concurrent Financial Time Series as Convolutional Nets.

We present a novel representation for multiple synchronized financial time series as images, motivated by deep learning methods in machine vision. The research pursues two related strands of inquiry. The first is to transform concurrent synchronized time series analysis-one that is prevalent in Finance and other domains-into a machine vision problem so that the standard deep learning machinery such as convolutional nets can be applied to the transformed problem. The second line of inquiry pursues the idea of transfer learning, where learning occurs on synthetic simulated data corresponding to a finite set of lead-lag relationships in the concurrent time series, and the learned model is applied out of the box to the application domain, in our case, Finance. The successful application of transfer learning, however, requires that a relationship exists between the simulated and real-world data that the learner is able to discern. This relationship helps to bias the learner toward learning things that will be useful in the application domain. We demonstrate the application of our trained model for identifying data-driven regime shifts in financial time series data. We present an analysis of the results and discuss some of the useful properties of the representation and directions for future research.