# Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series
## 2 Deep Learning in Cryptocurrency Forecasting: State-of-the-Art  
[PERSON] and [PERSON] [12] focused on the price non-stationary dynamics of three cryptocurrencies Bitcoin, Etherium, and Ripple. Their approach aimed at identifying and understand the factors which influence the value formation of these digital currencies. Their collected data contained 1030 trading days regarding opening, high, low, and closing prices. They conducted an experimental analysis which revealed the efficiency of LSTM models over classical ANNs, indicating that LSTM models are more capable of exploiting information hidden in historical data. Additionally, the authors stated that probably the reason for the efficiency of LSTM networks is that they tend to depend more on short-term dynamics while ANNs tends to depend more on long-term history. Nevertheless, in case enough historical information is given, ANNs can achieve similar accuracy to LSTM networks. [PERSON] et al. [13] examined the performance of ANNs for the prediction of Bitcoin intraday technical trading. The authors focused on identifying the key factors which affect the prediction performance for extracting useful trading signals of Bitcoin from its technical indicators. For this purposed, they conducted a series of experiments utilizing various ANN models with shallow and deep architectures and datasets structures The data utilized in their research regarded Bitcoin time-series return data at 15-min time intervals. Their experiments illustrated that the utilization of multiple technical indicators could possibly prevent the prediction model from overfitting of non-stationary financial data, which enhances trading performance. Moreover, they stated that their proposed methodology attained considerably better performance than the primitive technical trading and buy-and-hold strategies, under realistic assumptions of execution costs. [PERSON] et al. [14] utilized two deep learning models, namely a Bayesian-optimised Recurrent Neural Network and a LSTM network, for Bitcoin price prediction. The utilized data ranged from the August 2013 until July 2016, regarding open, high, low and close of Bitcoin prices as well as the block difficulty and hash rate. Their performance evaluation showed that the LSTM network demonstrated the best prediction accuracy, outperforming the other recurrent model as well as the classical statistical method ARIMA. [PERSON] and [PERSON] [15] proposed a new trend prediction classification framework which is based on deep learning techniques. Their proposed framework utilized a metric learning-based method, called Random Sampling method, which measures the similarity between the training samples and the input patterns. They used high frequency data (1-min) ranged from June 2013 to March 2017 containing historical data from OkCoin Bitcoin market (Chinese Yuan Renminbi and US Dollars). The authors concluded that the profit rates based on utilized sampling method considerably outperformed those based on LSTM networks, confirming the superiority of the proposed framework. In contrast, these profit rates were lower than those obtained of the classical buy-and-hold strategy; thus they stated that it does not provide a basis for trading. [PERSON] et al. [16] attempted to analyze the high-frequency Bitcoin (1-min) time series utilizing machine learning and statistical forecasting models. Due to the large size of the data, they decided to aggregate the realized volatility values utilizing 3-h long intervals. Additionally, they pointed out that these values presented a weak correlation based on high-low price extent with the relative values of the 3-h interval. In their experimental analysis, they focused on evaluating various ANNs-type models, SVMs and Ridge Regression and the Heterogeneous Auto-Regressive Realized Volatility model. Their results demonstrated that Ridge Regression considerably presented the best performance while SVM exhibited poor performance. [PERSON] et al.