Paper Title
BITCOIN MARKET PRICE PREDICTION USING NEURAL NETWORKS
Article Identifiers
: Keywords
Bitcoin, machine learning, prediction methods, and change detection algorithms are all included.
Abstract
Investors have recently become interested in cryptocurrency because of its inherent decentralization and transparency. In order to build efficient trading platforms, accurate value estimation is essential, given the cryptocurrencies' new features and volatility. The researchers propose a cutting-edge method for determining the value of Bitcoin (BTC), a well-known cryptocurrency, in order to accomplish this. The change point detection method is utilized to provide consistent prediction performance across previously unknown price ranges. Time-series data are split specifically so that segmentation-based normalization can be carried out separately. Price forecasting also makes use of on-chain data as an input variable. The various records that are contained in cryptocurrencies and saved on the blockchain are referred to as "on-chain data." Moreover, for on-chain variable assembles, this paper exhorts involving SAM-LSTM as the assumption model, which includes the thought part and a couple of LSTM modules. Self-consideration-based multiple long short-term memory is abbreviated as SAM-LSTM. Tests conducted with authentic BTC cost information and a variety of technique limits demonstrated that the proposed structure was effective in forecasting BTC prices. The highest individual values for the MAE, RMSE, MSE, and MAPE were 0.3462, 0.5035, 0.2536, and 1.3251, respectively. The outcomes are positive.
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How To Cite (APA)
Dr. A.RADHIKA, SUHANA PATHAN, SANA KAUSER, SANGOJU YOGENDRA, & SHAIK AAMIR FAIZ (April-2023). BITCOIN MARKET PRICE PREDICTION USING NEURAL NETWORKS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), c201-c209. https://ijnrd.org/papers/IJNRD2304225.pdf
Issue
Volume 8 Issue 4, April-2023
Pages : c201-c209
Other Publication Details
Paper Reg. ID: IJNRD_190554
Published Paper Id: IJNRD2304225
Downloads: 000122005
Research Area: Engineering
Author Type: Indian Author
Country: Vijayawada, Andhra pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2304225.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304225
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