基于数据挖掘预测算法的不可替代代币(nft)价格预测

Indri Tri Julianto, D. Kurniadi, Fakhrun Mahda Khoiriyyah
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引用次数: 2

摘要

2022年初,通过OpenSea的一个名为Ghozali Everyday的账户创建和销售的内容,非可替代代币(nft)在印度尼西亚经历了一个受欢迎的高峰。据报道,Ghozali从他创造的内容中赚取了±13亿卢比。这激发了印尼人民的好奇心,他们模仿Ghozali Everyday的做法,希望能获得类似的好处。nft的市场价格与股票价格相同,会根据加密货币的价格而波动,因为这些nft通常可以用加密货币购买,即以太坊。本研究采用数据挖掘预测算法对nft的价格进行预测。通过比较五种算法来找到最佳算法:深度学习、线性回归、神经网络、支持向量机和广义线性模型。使用的方法是数据库中的知识发现。nft价格数据集取自coinmarketcap.com页面,时间为2021年11月16日至2022年11月16日。结果表明,与其他算法相比,最佳的数据挖掘预测算法是神经网络,其均方根误差(RMSE)值最低,为83.617 +/- 18.853(微平均值:85.590 +/- 0.000)。在数据集中使用神经网络后,图表结果显示收盘价和预测价格之间没有显着差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Price Prediction of Non-Fungible Tokens (NFTs) using Data Mining Prediction Algorithm
Non-Fungible Tokens (NFTs) experienced a peak of popularity in Indonesia through content created and sold by an account at OpenSea called Ghozali Everyday in early 2022. Ghozali reportedly earned ± Rp. 1.3 billion from the content he has created. This sparked the curiosity of the Indonesian people to imitate what Ghozali Everyday did in the hope of getting similar benefits. The market price of NFTs is the same as stock prices, which will fluctuate depending on the price of the cryptocurrency because these NFTs can generally be purchased with the cryptocurrency, namely Ethereum. This research was conducted to predict the price of NFTs using the Data Mining Prediction Algorithm. Five algorithms are compared to find the best algorithm: Deep Learning, Linear Regression, Neural Networks, Support Vector Machines, and Generalized Linear Model. The methodology used is Knowledge Discovery in Databases. The NFTs price dataset is taken from the page coinmarketcap.com from 16 November 2021 to 16 November 2022. The results show that the best Data Mining Prediction Algorithm is a Neural Network with a value of The lowest Root Mean Square Error (RMSE) compared to other algorithms, namely 83.617 +/- 18.853 (micro average: 85.590 +/- 0.000). After the Neural Network is used in the Dataset, the graph results show no significant difference between the Closing Price and the Predicted Price.
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