Analyzing Deep Generated Financial Time Series for Various Asset Classes
50 Pages Posted: 30 Aug 2021
Date Written: August 3, 2021
Abstract
Generative Adversarial Networks (GANs) have shown remarkable success as a framework for trainingmodels to produce realistic-looking data. In this work, we propose a GAN to produce realistic real-valued time series, with an emphasis on their application to financial data.Our aim is having a GAN, applied on various financial time series for various asset classes, that canreflect all the characteristics of them, as well as the characteristics we may be unaware of, as GANslearn the underlying structure of our data, rather than just a set of features. If we are able to achievethis, the synthetic datasets we create could be used for a variety of purposes including model trainingand model selection. In this paper we try to train a GAN with real data, representing one asset ofdifferent asset classes such as commodities, forex, futures, index and shares.
Keywords: Time Series Classification, Generative Adversarial Networks, Financial Time Series
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