Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This Study focuses on time series analysis for predicting Bitcoin prices using various methodologies including recurrent neural network (RNN), Long short term memory (LSTM), auto regressive integrity integrated moving average (ARIMA) and Facebook’s prophet. We utilize a dataset consisting of time stamps and closing prices to train and evaluate the performance of these models. The objective is to identify the most effective forecasting technique for Bitcoin Price moments, addressing the inherent volatility of cryptocurrency markets by leveraging historical price data, preimage to enhance prediction accuracy contributing to more informed trading decisions. Our finding will provide valuable insights Into the applicability of different predictive models in the context of cryptocurrency ultimately aiming to assist investors in navigating the complexities of Bitcoin trading. The results underscore the strength and weaknesses of each method paving the way for future research in financial tying series analysis.
Keywords:
RNN, LSTM, ARIMA, and Prophet, Kaggle dataset
Cite Article:
"CRYPTO CURRENCY PRICE PREDICTION USING DEEP LEARNING", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.a653-a658, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504084.pdf
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000357
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator