# Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series
## 2 Deep Learning in Cryptocurrency Forecasting: State-of-the-Art
[17] evaluated the prediction performance on Bitcoin price of various deep learning models such as LSTM networks, convolutional neural networks, deep neural networks, deep residual networks and their combinations. The data used in their research, contained 29 features of the Bitcoin blockchain from 2590 days (from 29 November 2011 to 31 December 2018). They conducted a detailed experimental procedure considering both classification and regression problems, where the former predicts whether or not the next day price will increase or decrease and the latter predicts the next day's Bitcoin price. The numerical experiments illustrated that the deep neural DNN-based models performed the best for price ups-and-downs while the LSTM models slightly outperformed the rest of the models for forecasting Bitcoin's price. [PERSON] and [PERSON] [18] focused on forecasting the trends of Etherium prices utilizing machine learning and deep learning methodologies. They conducted an experimental analysis and compared the prediction ability of LSTM neural networks and Multi-Layer perceptron (MLP). They utilized daily, hourly, and minute based data which were collected from the CoinMarket and CoinDesk repositories. Their evaluation results illustrated that LSTM marginally outperformed MLP but not considerably, although their training time was significantly high. [PERSON] et al. [7,11] conducted a detailed research, evaluating advanced deep learning models for predicting major cryptocurrency prices and movements. Additionally, they conducted a detailed discussion regarding the fundamental research questions: Can deep learning algorithms efficiently predict cryptocurrency prices? Are cryptocurrency prices a random walk process? Which is a proper validation method of cryptocurrency price prediction models? Their comprehensive experimental results revealed that even the LSTM-based and CNN-based models, which are generally preferable for time-series forecasting [8, 9, 10], were unable to generate efficient and reliable forecasting models. Moreover, the authors stated that cryptocurrency prices probably follow an almost random walk process while few hidden patterns may probably exist. Therefore, new sophisticated algorithmic approaches should be considered and explored for the development of a prediction model to make accurate and reliable forecasts. In this work, we advocate combining the advantages of ensemble learning and deep learning for forecasting cryptocurrency prices and movement. Our research contribution aims on exploiting the ability of deep learning models to learn the internal representation of the cryptocurrency data and the effectiveness of ensemble learning for generating powerful forecasting models by exploiting multiple learners for reducing the bias or variance of error. Furthermore, similar to our previous research [7, 11], we provide detailed performance evaluation for both regression and classification problems. To the best of our knowledge, this is the first research devoted to the adoption and combination of ensemble learning and deep learning for forecasting cryptocurrencies prices and movement.