  Modeling financial time series is challenging due to their high volatility
and unexpected happenings on the market. Most financial models and algorithms
trying to fill the lack of historical financial time series struggle to perform
and are highly vulnerable to overfitting. As an alternative, we introduce in
this paper a deep neural network called the WGAN-GP, a data-driven model that
focuses on sample generation. The WGAN-GP consists of a generator and
discriminator function which utilize an LSTM architecture. The WGAN-GP is
supposed to learn the underlying structure of the input data, which in our
case, is the Bitcoin. Bitcoin is unique in its behavior; the prices fluctuate
what makes guessing the price trend hardly impossible. Through adversarial
training, the WGAN-GP should learn the underlying structure of the bitcoin and
generate very similar samples of the bitcoin distribution. The generated
synthetic time series are visually indistinguishable from the real data. But
the numerical results show that the generated data were close to the real data
distribution but distinguishable. The model mainly shows a stable learning
behavior. However, the model has space for optimization, which could be
achieved by adjusting the hyperparameters.
