International Journal of Advanced Engineering Application

Model for Predicting Non-Fungible Token Prices Through Time Series Analysis

Author(s):Mahesh Kumar�, Bajali Yamamura�

Affiliation: 1,2 Student, Siddharth Institute of Engineering & Technology, India.

Page No: 1-12

Volume issue & Publishing Year: Volume 1 Issue 3,July-2024

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI:

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Abstract:
In the rapidly evolving cryptocurrency and digital art markets, predicting the prices of Non-Fungible Tokens (NFTs) presents a complex and dynamic challenge. To address this, we have developed a prediction model utilizing time series analysis and machine learning techniques. Our approach involves gathering historical NFT price data, preprocessing it to handle outliers and missing values, and applying an LSTM-based time series forecasting model. Our findings indicate that this model significantly outperforms benchmark models, offering superior forecasting accuracy.

This research holds important implications for NFT investors, collectors, and market analysts, providing a valuable tool for informed decision-making. The model aids in risk assessment, investment strategy formulation, and market trend analysis. Additionally, our study highlights the potential of time series analysis in price forecasting within the fast-paced NFT market, paving the way for further exploration in this emerging field.

Keywords: NFT (Non-Fungible Token), Price Prediction, Time Series Analysis, Cryptocurrency Market, Machine Learning Ethereum platform.

Reference:

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