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.

中文翻译：

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将金融转化为愿景：作为卷积网的并行财务时间序列

我们以机器视觉中的深度学习方法为动力，以图像的形式显示了多个同步财务时间序列的新颖表示形式。该研究遵循两个相关的探究链。首先是将并发同步时间序列分析（在金融和其他领域中很普遍）转换为机器视觉问题，以便将标准的深度学习机器（例如卷积网）应用于转换后的问题。第二行查询采用转移学习的思想，其中学习是在与并行时间序列中的一组有限的超前-滞后关系相对应的合成模拟数据上进行的，并且将学习的模型直​​接应用于应用程序域，在我们的例子中，财务。但是，成功应用了转移学习 要求学习者能够辨别的模拟数据和真实数据之间存在关系。这种关系有助于使学习者偏向于学习对应用程序领域有用的东西。我们展示了我们训练有素的模型在识别金融时间序列数据中数据驱动的制度转变中的应用。我们对结果进行了分析，并讨论了表示的一些有用特性和未来研究的方向。