Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/3969
Title: Fusion in cryptocurrency price prediction: a decade survey on recent advancements, architecture, and potential future directions
Authors: Patel, Nisarg P.
Parekh, Raj 
Thakkar, Nihar 
Gupta, Rajesh
Tanwar, Sudeep
Sharma, Gulshan
Davidson, Innocent E.
Sharma, Ravi
Keywords: Bitcoin;Predictive models;Forecasting;Prediction algorithms;Peer-to-peer computing;Costs;Deep learning;Cryptocurrency;Price analysis;Volume analysis;Deep learning;Machine learning;Survey;Ensemble model;Fusion in metrics;08 Information and Computing Sciences;09 Engineering;10 Technology
Issue Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Source: Patel, N.P. et al. 2022. Fusion in cryptocurrency price prediction: a decade survey on recent advancements, architecture, and potential future directions. IEEE ACCESS. 10: 34511-34538. doi:10.1109/ACCESS.2022.3163023
Journal: IEEE ACCESS; Vol. 10 
Abstract: 
Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However, Dash coin is an individual cryptocurrency, but it is derived from Bitcoin and Litecoin. The change in Bitcoin and Litecoin prices affects the Dash coin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction of Dash coin through the past days’ prices of Dash, Litecoin, and Bitcoin as they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations.
URI: https://hdl.handle.net/10321/3969
ISSN: 2169-3536 (Online)
DOI: 10.1109/ACCESS.2022.3163023
Appears in Collections:Research Publications (Engineering and Built Environment)

Files in This Item:
File Description SizeFormat
Patel et al_2022.pdfarticle3.39 MBAdobe PDFView/Open
Show full item record

Page view(s)

28
checked on Jun 28, 2022

Download(s)

32
checked on Jun 28, 2022

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.