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How To Predict Cryptocurrency Prices
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Band Cryptocurrency Price Prediction
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Received: 19 June 2022 / Revised: 19 August 2022 / Accepted: 15 September 2022 / Published: 31 October 2022
How Crypto Transforms Prediction Markets
Specific funds are recognized as one of the financial assets that are widely recognized as convertible. Cryptocurrency trading has attracted investors because the money spent can be considered a better investment. In order to optimize the return on investment, it is necessary to estimate prices accurately. Considering the fact that the price is a time series, a deep learning model is introduced to predict the price of cryptocurrencies in the future. The hybrid model combines a 1-dimensional convolutional neural network with a closed-loop clustering algorithm (1DCNN-GRU). Given cryptocurrency data over time, a 1-dimensional convolutional neural network encodes the data with a high degree of discrimination. Finally, joint closed-loop control captures the long-term dependence of the agent. The test model was evaluated on three different cryptocurrency databases called Bitcoin, Ethereum and Ripple. Experimental results showed that the tested 1DCNN-GRU model outperformed existing methods with the lowest RMSE value of 43.933 on the Bitcoin dataset, 3.511 on the Ethereum dataset, and 0.00128 on the Ripple dataset.
Cryptocurrency works as a peer-to-peer digital currency where all transactions are done in a secure manner. Transactions are further stored in a block, called a blockchain. Security has made cryptocurrency a popular and popular trading platform for investors. Cryptocurrency is growing rapidly, gaining popularity and capitalization. Bitcoin is the first digital currency created by Satoshi Nakamoto  and has become the world’s most valuable digital asset. Along with the large number of financial transactions, various types of money have started in the world of information. Some popular ones are Ethereum and Ripple, among others.
This study focuses on the prediction of cryptocurrency prices. Cryptocurrency price forecasting is a time series problem that can be solved using deep learning techniques. Although cryptocurrency price prediction is challenging, the development of cryptocurrency price prediction algorithms is beneficial as it plays an important role for users. Inspired by the success of deep learning models in many applications, this paper combines a 1-dimensional convolutional neural network (1DCNN) with a closed-loop aggregate (GRU) in 1DCNN-GRU. Example for predicting the price of cryptocurrency. The three historical stock price data are mainly collected from the exchange website. Finally, the datasets are preprocessed for classification and removal of missing values before passing the 1DCNN-GRU model for training models and prediction values. 1DCNN layer plays the role of detecting special features in historical data. The resulting features are sent to a built-in GRU for physical processes where long-term dependencies are captured. Time signals are used to estimate the price of cryptocurrencies. The predicted value is compared with the actual value and the root mean square error is calculated. The main contributions of this paper are as follows.
This section describes some of the functions that exist in the field of cryptocurrency pricing. There are many deep learning models that have been used for price prediction [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14].
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In early work, Pap et al. (2017)  proposed a model algorithm that can be used in finance, engineering and medicine. The algorithm combines Artificial Neural Network (ANN) and Multilayer Perceptron (MLP). From experiments, adding MLP to ANN increased the accuracy of Bitcoin price from 58% to 63%.
A deep learning algorithm called Facebook oracle for bitcoin price was used by Yenidogan et al. (2018) . A triple partitioning procedure was performed to ensure optimality for the training, test and validation sets. Experimental results showed that the PROPHET algorithm was superior to the ARIMA algorithm, where PROPHET achieved a root mean square error (RMSE) of 652.18 compared to 817.01.
McNally et al. (2018)  used several deep learning algorithms to estimate the price of Bitcoin. At the beginning of the process, working designs were derived from data by technical engineering. The experimental results showed that the long-short time model (LSTM) achieved the highest accuracy of 52.78%, while the recurrent neural network (RNN) achieved the lowest accuracy of 5.45%.
Different recovery methods were presented by Fladisilode et al. (2018)  to predict Bitcoin prices using Keras and Scit-Learn libraries. The data is obtained from Kaggle which includes a minute interval of data from Bitcoin exchange website Bitstamp. The best results showed that R-squared of 99.2% was obtained from LSTM and GRU models.
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Jiang (2020)  proposed deep learning methods to predict bitcoin prices by collecting and reconstructing bitcoin price data from minutes to hours. Data were first grouped, followed by min-max normalization before entering mini-series and regression models. The project introduced several deep learning networks such as MLP, RNN and LSTM extensions and GRU to predict the future price of Bitcoin. Experimental results showed that the MLP model with the inclusion of two tables of GRU gave the best result, with the smallest RMSE of 19,020.
Politis et al. (2021)  used several deep learning models to predict the price of Ethereum. The decision was made to reduce the complexity and complexity of the data. The simulation model was implemented with a combination of LSTM, GRU and/or temporal perturbation network (TCN). In the daily simulation model, the model with LSTM, GRU and hybrid GRU-TCN had the best performance of 84.2% accuracy, but the LSTM-GRU model achieved the lowest RMSE of 8.6.
Another LSTM-GRU hybrid model was presented by Tanwar et al. (2021)  on the financial perspective. The project considers Bitcoin as the main currency and traces the direction of Bitcoin’s value. Finally, movement strategy was used to predict the price of Litecoin and Zcash with the idea of cooperation between Bitcoin-Litecoin and Bitcoin-Zcash. With a window of one day, the LSTM-GRU model recorded a mean square error (MSE) of 0.02038 for Litecoin and 0.00461 for Zcash.
Livieris et al. (2021)  presented a deep learning model of cryptocurrency, also known as MICDL. The model used each cryptocurrency index as input to a mutual fund, followed by a mutual fund and LSTM layer. Common constructs of deep learning neural networks such as density, hierarchical structure, diversity and induction were used. CNN computation with LSTM layer achieved 55.03% accuracy on Bitcoin data, while 51.51% accuracy on Ethereum data and 49.61% accuracy on Ripple data.
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Zhang et al. (2021)  presented a weighted and alerted convolutional neural network (WAMC) for cryptocurrency price prediction. The model consists of a GRU that holds attentional memory for each input sequence, a measure of learning. Who is the interplay between multiple currencies, and CNN provides physical insights of historical data. The estimated WAMC recorded an RMSE of 9.70 for Ethereum and 1.37 for Bitcoin.
Jay et al. (2020)  built stochastic neural networks to predict the price of Bitcoin, Ethereum and Litecoin. The work considered three things, such as exchange market statistics, blockchain data and social sentiment as an application for neural networks. To explain the disturbance in values, stochastic tables were combined with MLP and LSTM models. Compared to deterministic MLP and LSTM, stochastic neural networks (MLP and LSTM) showed an improvement of 4.84833% for Bitcoin, 4.15640% for Ethereum and 4.74619% for Litecoin.
The cost of equity prediction by Sebastiao et al. (2021) .