Style Transfer with Time Series: Generating Synthetic Financial Data

Abstract

Training deep learning models that generalize well to live deployment is a challenging problem in the financial markets. The challenge arises because of high dimensionality, limited observations, changing data distributions, and a low signal-to-noise ratio. High dimensionality can be dealt with using robust feature selection or dimensionality reduction, but limited observations often result in a model that overfits due to the large parameter space of most deep neural networks. We propose a generative model for financial time series, which allows us to train deep learning models on millions of simulated paths. We show that our generative model is able to create realistic paths that embed the underlying structure of the markets in a way stochastic processes cannot.

References in corpus (5)

• Auto-Encoding Variational Bayes
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• The Effectiveness of Data Augmentation in Image Classification using Deep Learning
• VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
• Data augmentation using synthetic data for time series classification with deep residual networks