Non-fungible token price prediction with multivariate LSTM neural networks
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Publication date
2022
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04B - Conference paper
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2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)
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Pages / Duration
56-61
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IEEE
Place of publication / Event location
Toronto
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Abstract
In this paper, we investigate how to forecast Non-Fungible Token (NFT) sale prices by using multiple multivariate time series datasets containing features related to the NFT market space. We examined eight recent studies regarding the forecasting and valuation of NFTs and compared their most important findings. This laid the fundamental work for two separate machine learning prototypes based on Long Short-Term Memory (LSTM) which are able to forecast the sale price history of an individual NFT asset. Root Mean Squared Errors (RMSE) of 0.2975 and 0.24 were obtained which appears to be promising.
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Soft Computing & Machine Intelligence (ISCMI)
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979-8-3503-2088-6
979-8-3503-2087-9
979-8-3503-2087-9
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Language
English
Created during FHNW affiliation
Yes
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Published
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Closed
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Citation
Branny, J., Dornberger, R., & Hanne, T. (2022). Non-fungible token price prediction with multivariate LSTM neural networks. 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), 56–61. https://doi.org/10.1109/ISCMI56532.2022.10068442