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

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 var...

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Main Authors: Shamshad, H., Ullah, F., Ullah, A., Kebande, V.R., Ullah, S., Al-Dhaqm, A.
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Online Access:http://scholars.utp.edu.my/id/eprint/38011/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176343169&doi=10.1109%2fACCESS.2023.3327440&partnerID=40&md5=50ce468cc316983814a0cc5d6ded2ac5
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spelling oai:scholars.utp.edu.my:380112023-12-11T03:09:27Z http://scholars.utp.edu.my/id/eprint/38011/ Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions Shamshad, H. Ullah, F. Ullah, A. Kebande, V.R. Ullah, S. Al-Dhaqm, A. 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. Institute of Electrical and Electronics Engineers Inc. 2023 Article NonPeerReviewed 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 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176343169&doi=10.1109%2fACCESS.2023.3327440&partnerID=40&md5=50ce468cc316983814a0cc5d6ded2ac5 10.1109/ACCESS.2023.3327440 10.1109/ACCESS.2023.3327440 10.1109/ACCESS.2023.3327440
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description 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.
format Article
author Shamshad, H.
Ullah, F.
Ullah, A.
Kebande, V.R.
Ullah, S.
Al-Dhaqm, A.
spellingShingle Shamshad, H.
Ullah, F.
Ullah, A.
Kebande, V.R.
Ullah, S.
Al-Dhaqm, A.
Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions
author_facet Shamshad, H.
Ullah, F.
Ullah, A.
Kebande, V.R.
Ullah, S.
Al-Dhaqm, A.
author_sort Shamshad, H.
title Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions
title_short Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions
title_full Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions
title_fullStr Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions
title_full_unstemmed Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions
title_sort forecasting and trading of the stable cryptocurrencies with machine learning and deep learning algorithms for market conditions
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/38011/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176343169&doi=10.1109%2fACCESS.2023.3327440&partnerID=40&md5=50ce468cc316983814a0cc5d6ded2ac5
_version_ 1787138255127314432
score 13.211869

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