Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions

Shamshad, H. and Ullah, F. and Ullah, A. and Kebande, V.R. and Ullah, S. and Al-Dhaqm, A. (2023) Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions. IEEE Access, 11. pp. 122205-122220. ISSN 21693536

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The digital market trend is rapidly expanding due to key characteristics like decentralization, accessibility, and market diversity enabled by blockchain technology. This study proposes a Predictive Analytics System to provide simplified reporting for the three most popular cryptocurrencies with varying digits, namely ADA Cardano, Ethereum, and Binance coin, for ten days to contribute to this emerging technology. Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. Moreover, the research experiments are repeated several times to achieve the best results by employing hyperparameter tuning of each algorithm. This involves selecting an appropriate kernel and suitable data normalization technique for SVR, determining ARIMA's (p, d, q) values, and optimizing the loss function values, number of neurons, hidden layers, and epochs in LSTM models. For the model validation, we utilize widely used evaluation techniques: Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and R-squared. Results demonstrate that ARIMA outperforms the other models in all cases, accurately projecting the price variability within the actual price range. Conversely, Facebook Prophet exhibits good performance to some extent. The paper suggests that the ARIMA technique offers practical implications for market analysts, enabling them to make well-informed decisions based on accurate price projections. © 2013 IEEE.

Item Type: Article
Impact Factor: cited By 0
Uncontrolled Keywords: Bitcoin; Costs; Errors; Ethereum; Learning algorithms; Long short-term memory; Mean square error; Predictive analytics; Support vector machines, ADA cardano; Autoregressive integrated moving average(ARIMA); Bidirectional LSTM; Binance; Biological system modeling; Block-chain; Cryptocurrency forecasting; Deep learning; Facebook; Facebook prophet; Machine-learning; Prediction algorithms; Predictive models; Support vector regressor; Support vectors machine; Time series forecasting; Unidirectional LSTM, Blockchain
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 11 Dec 2023 03:09
Last Modified: 11 Dec 2023 03:09

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