  Bitcoin as a cryptocurrency has been one of the most important digital coins
and the first decentralized digital currency. Deep neural networks, on the
other hand, has shown promising results recently; however, we require huge
amount of high-quality data to leverage their power. There are some techniques
such as augmentation that can help us with increasing the dataset size, but we
cannot exploit them on historical bitcoin data. As a result, we propose a
shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data
using our proposed method to forecast bitcoin closing prices in a daily time
frame. We compare the performance with that of other forecasting methods, and
show that with the help of the proposed feature engineering method, a shallow
deep neural network outperforms other popular price forecasting models.

Statistical Finance;Artificial Intelligence;Machine Learning;Signal Processing