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Cryptoforecast: A Comparative Analysis of AI Models in Cryptocurrency Price Prediction

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Cryptoforecast: A Comparative Analysis of AI Models in Cryptocurrency Price Prediction


Ayush Bais | Nirbhay Headau | Prof. Anupam Chaube



Ayush Bais | Nirbhay Headau | Prof. Anupam Chaube "Cryptoforecast: A Comparative Analysis of AI Models in Cryptocurrency Price Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Emerging Trends and Innovations in Web-Based Applications and Technologies, January 2025, pp.623-626, URL: https://www.ijtsrd.com/papers/ijtsrd75060.pdf

The volatile nature of cryptocurrency markets presents a significant challenge for traders and investors seeking reliable price forecasts. Recent advancements in artificial intelligence (AI) have led to the development of various predictive models aimed at improving accuracy in cryptocurrency price prediction. This study provides a comparative analysis of AI models used for cryptocurrency forecasting, including machine learning approaches such as Support Vector Machines (SVM), Random Forest (RF), and deep learning techniques like Long Short-Term Memory (LSTM) networks, Transformer-based models, and hybrid ensembles. The analysis evaluates each model's performance based on key metrics such as mean absolute error (MAE), root mean square error (RMSE), and directional accuracy. Additionally, factors influencing model efficacy, such as feature selection, data preprocessing, and market sentiment integration, are explored. Findings indicate that deep learning models, particularly LSTM and Transformer-based architectures, exhibit superior performance in capturing the non-linear dependencies and temporal patterns of cryptocurrency markets. However, hybrid models integrating multiple AI techniques show promise in enhancing prediction robustness. This research underscores the importance of model selection and data preprocessing in optimizing cryptocurrency price predictions and offers insights into future developments in AI-driven financial forecasting.

Forecast, Prediction, Artificial Intelligence, Investment, currency, Cryptography, Analysis, Security, Valuation, Strategy


IJTSRD75060
Special Issue | Emerging Trends and Innovations in Web-Based Applications and Technologies, January 2025
623-626
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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