IJRTI
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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)

Call For Paper

For Authors

Forms / Download

Published Issue Details

Editorial Board

Other IMP Links

Facts & Figure

Impact Factor : 8.14

Issue per Year : 12

Volume Published : 10

Issue Published : 115

Article Submitted : 19459

Article Published : 8041

Total Authors : 21252

Total Reviewer : 769

Total Countries : 145

Indexing Partner

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: CRYPTO CURRENCY PRICE PREDICTION USING DEEP LEARNING
Authors Name: PATOORU BINDU , RAMESH HEMAVATHI , PULLAIH KEERTHI , KUNA LIKITHA , A. VENKATESAN
Download E-Certificate: Download
Author Reg. ID:
IJRTI_202189
Published Paper Id: IJRTI2504084
Published In: Volume 10 Issue 4, April-2025
DOI:
Abstract: 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
Downloads: 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
Publication Details: Published Paper ID: IJRTI2504084
Registration ID:202189
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: a653-a658
Country: CHITTOOR, ANDHRA PRADESH, India
Research Area: Science & Technology
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504084
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504084
Share Article:

Click Here to Download This Article

Article Preview
Click Here to Download This Article

Major Indexing from www.ijrti.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

ISSN Details

ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

DOI (A digital object identifier)


Providing A digital object identifier by DOI.ONE
How to Get DOI?

Conference

Open Access License Policy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Join RMS/Earn 300

IJRTI

WhatsApp
Click Here

Indexing Partner