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Study of Swarm Intelligence Algorithms for Optimizing Deep Neural Network for Bitcoin Prediction

Study of Swarm Intelligence Algorithms for Optimizing Deep Neural Network for Bitcoin Prediction

S. Aarif Ahamed, Chandrasekar Ravi
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 17
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781799861331|DOI: 10.4018/IJSIR.2021040102
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MLA

Ahamed, S. Aarif, and Chandrasekar Ravi. "Study of Swarm Intelligence Algorithms for Optimizing Deep Neural Network for Bitcoin Prediction." IJSIR vol.12, no.2 2021: pp.22-38. http://doi.org/10.4018/IJSIR.2021040102

APA

Ahamed, S. A. & Ravi, C. (2021). Study of Swarm Intelligence Algorithms for Optimizing Deep Neural Network for Bitcoin Prediction. International Journal of Swarm Intelligence Research (IJSIR), 12(2), 22-38. http://doi.org/10.4018/IJSIR.2021040102

Chicago

Ahamed, S. Aarif, and Chandrasekar Ravi. "Study of Swarm Intelligence Algorithms for Optimizing Deep Neural Network for Bitcoin Prediction," International Journal of Swarm Intelligence Research (IJSIR) 12, no.2: 22-38. http://doi.org/10.4018/IJSIR.2021040102

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Abstract

Blockchain, a shared digital ledger, operates on a peer-to-peer network which is used for storing the transactions. Cryptocurrencies are used for transactions in blockchain. The most popular breed among cryptocurrency was bitcoin. Predicting the day-to-day value of bitcoin is a challenging task due to nonlinear and market volatility. There are many statistical methods and machine learning algorithms proposed to forecast the cost of bitcoin, but they were lacking to predict the correct result when the input data set is larger and has more noise. To handle large data set, a deep learning technique has been used. The deep learning algorithms, especially LSTM network, also have some drawbacks such as high computational time, inability to generate higher quality prediction result. To avoid these shortcomings and make LSTM a better model for bitcoin prediction, it is necessary to optimize LSTM network. This paper presents a comparative study of numerous optimized deep learning techniques to forecast the price of bitcoin.

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