Page 633 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD)
Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies
Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470

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

1
2
Ayush Bais , Nirbhay Headau , Prof. Anupam Chaube
3
1,2,3 Department of Science and Technology,
1,2 G H Raisoni Institute of Engineering and Technology, Nagpur, Maharashtra, India
3 G H Raisoni College of Engineering and Management, Nagpur, Maharashtra, India

ABSTRACT Short-Term Memory (LSTM) networks and Transformer-
The volatile nature of cryptocurrency markets presents a based architectures, are designed to capture sequential
significant challenge for traders and investors seeking dependencies and complex temporal relationships, making
reliable price forecasts. Recent advancements in artificial them well-suited for financial time series forecasting.
intelligence (AI) have led to the development of various Additionally, hybrid models that combine multiple AI
predictive models aimed at improving accuracy in techniques have gained attention for their ability to enhance
cryptocurrency price prediction. This study provides a predictive robustness and reduce overfitting.
comparative analysis of AI models used for cryptocurrency
forecasting, including machine learning approaches such as What is crypto currency
Support Vector Machines (SVM), Random Forest (RF), and Cryptocurrencies are digital or virtual currencies
deep learning techniques like Long Short-Term Memory underpinned by cryptographic systems. They enable secure
(LSTM) networks, Transformer-based models, and hybrid online payments without the use of third-party
ensembles. The analysis evaluates each model's intermediaries. "Crypto" refers to the various encryption
performance based on key metrics such as mean absolute algorithms and cryptographic techniques that safeguard
error (MAE), root mean square error (RMSE), and these entries, such as elliptical curve encryption, public-
directional accuracy. Additionally, factors influencing model private key pairs, and hashing functions.Central to the appeal
efficacy, such as feature selection, data preprocessing, and and functionality of Bitcoin and other cryptocurrencies is
market sentiment integration, are explored. Findings blockchain technology. As its name indicates, a blockchain is
indicate that deep learning models, particularly LSTM and essentially a set of connected blocks of information on an
Transformer-based architectures, exhibit superior online ledger. Each block contains a set of transactions that
performance in capturing the non-linear dependencies and have been independently verified by each validator on a
temporal patterns of cryptocurrency markets. However, network.Every new block generated must be verified before
hybrid models integrating multiple AI techniques show being confirmed, making it almost impossible to forge
promise in enhancing prediction robustness. This research transaction histories. The contents of the online ledger must
underscores the importance of model selection and data be agreed upon by a network of individual nodes, or
preprocessing in optimizing cryptocurrency price computers that maintain the ledger.Experts say that
predictions and offers insights into future developments in blockchain technology can serve multiple industries, supply
AI-driven financial forecasting. chains, and processes such as online voting and
crowdfunding. Financial institutions such as JPMorgan Chase
& Co. (JPM) are using blockchain technology to lower
KEYWORDS: Forecast, Prediction, Artificial Intelligence, transaction costs by streamlining payment processing.
Investment, currency, Cryptography, Analysis, Security,
Valuation, Strategy Crypto forecast
CryptoForecast is generally used to describe the prediction
INTRODUCTION or analysis of cryptocurrency market trends, prices, and
Cryptocurrency markets are known for their high volatility, movements. Some platforms or websites may specifically be
making accurate price prediction a challenging task for named CryptoForecast, offering tools and predictions to help
traders, investors, and financial analysts. Traditional traders and investors make decisions. These platforms
forecasting methods, such as statistical models and technical typically analyse vast amounts of data to offer short- and
analysis, often struggle to capture the complex, non-linear long-term predictions, although they cannot guarantee
patterns that characterize cryptocurrency price movements. accuracy due to the volatility of the cryptocurrency market.
As a result, artificial intelligence (AI) has emerged as a It may refer to various tools, platforms, or models that aim to
powerful tool for improving prediction accuracy by forecast the future price and market behaviour of
leveraging advanced machine learning (ML) and deep cryptocurrencies based on different analytical techniques,
learning (DL) techniques. such as :-

In recent years, various AI models have been developed to  Technical Analysis: Examining price charts, trends, and
forecast cryptocurrency prices, each with distinct advantages historical data to predict future movements.
and limitations. Machine learning approaches, such as  Sentiment Analysis: Analysing social media, news, and
Support Vector Machines (SVM) and Random Forest (RF), other sources to gauge market sentiment and predict
rely on historical data and feature engineering to identify price fluctuations.
predictive patterns. Deep learning models, particularly Long

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