NFT price and sales characteristics prediction by transfer learning of visual attributes

Name: NFT price and sales characteristics prediction by transfer learning of visual attributes
Author: Sefer, Emre, Pala, Mustafa

İsim	NFT price and sales characteristics prediction by transfer learning of visual attributes
Yazar	Sefer, Emre, Pala, Mustafa
Basım Tarihi:	2024-12
Basım Yeri	- Elsevier
Konu	Temporal price prediction, Transfer learning, Deep learning, Blockchain, NFTs
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2405-9188
Kayıt Numarası	5fc45ff2-987f-4875-92f1-60c68f5eddf2
Lokasyon	Computer Science
Tarih	2024-12
Örnek Metin	Non-fungible tokens (NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. There was a recent surge of interest in trading them which makes them another type of alternative investment. The inherent volatility of NFT prices, attributed to factors such as over-speculation, liquidity constraints, rarity, and market volatility, presents challenges for accurate price predictions. For such analysis and forecasting, machine learning methods offer a robust solution framework. Here, we focus on three related prediction problems over NFTs: Predicting NFTs sale price, inferring whether a given NFT will participate in a secondary sale, and predicting NFT's sale price change over time. We analyze and learn the visual characteristics of NFTs by deep pre-trained models and combine such visual knowledge with additional important non-visual attributes such as the sale history, seller's and buyer's centralities in the trading network, and collection's resale probability. We categorize input NFTs into six categories based on their characteristics. Across detailed experiments, we found visual attributes obtained from deep pre-trained models to increase the prediction performance in all cases, and EfficientNet seems to perform the best. In general, CNN and XGBoost consistently outperformed the rest of them across all categories. We also publish our novel NFT dataset with temporal price knowledge, which is the first dataset to have NFT prices over time rather than at a single time point.
DOI	10.1016/j.jfds.2024.100148
Cilt	10

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NFT price and sales characteristics prediction by transfer learning of visual attributes

Yazar Sefer, Emre, Pala, Mustafa

Basım Tarihi 2024-12

Basım Yeri - Elsevier

Konu Temporal price prediction, Transfer learning, Deep learning, Blockchain, NFTs

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2405-9188

Kayıt Numarası 5fc45ff2-987f-4875-92f1-60c68f5eddf2

Lokasyon Computer Science

Tarih 2024-12

Örnek Metin Non-fungible tokens (NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. There was a recent surge of interest in trading them which makes them another type of alternative investment. The inherent volatility of NFT prices, attributed to factors such as over-speculation, liquidity constraints, rarity, and market volatility, presents challenges for accurate price predictions. For such analysis and forecasting, machine learning methods offer a robust solution framework. Here, we focus on three related prediction problems over NFTs: Predicting NFTs sale price, inferring whether a given NFT will participate in a secondary sale, and predicting NFT's sale price change over time. We analyze and learn the visual characteristics of NFTs by deep pre-trained models and combine such visual knowledge with additional important non-visual attributes such as the sale history, seller's and buyer's centralities in the trading network, and collection's resale probability. We categorize input NFTs into six categories based on their characteristics. Across detailed experiments, we found visual attributes obtained from deep pre-trained models to increase the prediction performance in all cases, and EfficientNet seems to perform the best. In general, CNN and XGBoost consistently outperformed the rest of them across all categories. We also publish our novel NFT dataset with temporal price knowledge, which is the first dataset to have NFT prices over time rather than at a single time point.

DOI 10.1016/j.jfds.2024.100148

Cilt 10