A Data-driven Deep Learning Approach for Bitcoin Price Forecasting
This work addresses price prediction for cryptocurrency traders, but it is incremental as it applies a known neural network architecture with custom features to a specific domain.
The paper tackled Bitcoin price forecasting by proposing a shallow Bidirectional-LSTM model with a novel feature engineering method, achieving superior performance compared to other forecasting models.
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.