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 |Pages: 17
DOI: 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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Introduction

For a long time, most of the accounting details are maintained using ledgers. A ledger is a primary accounting document which is used to store the day to day transactions carried out by the participants with the help of an intermediaries such as bank, etc. Participants are the people (suppliers, customers, producers, stakeholders, etc.) who have the privileges on their assets such as land, cash, equipment, etc. Ledgers are subjected to high-risk factors. Due to a lack of transparency, the ledgers can be easily misused and tampered. So it is necessary to secure day to day transactions efficiently. Today’s world is moving towards digitalization, so there is a need for a digital network to secure the day to day transaction effectively.

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