Non-Fungible Token forecast based on LSTM
Ran Ma, Xiaotian Yang, Fei Gao · 2022
As Bored Ape Yacht Club attracts more and more attention, the non-financially token based on blockchain technology also attracts our attention. Based on MetaWorld data in NFT as an example, this paper uses MetaWorld as prediction data to predict the trend of heterogeneous tokens. However, due to the short development time of NFT and its strong time influence factors, we introduce Long-Short Term Memory to predict the transaction data in NFT. By comparing the prediction with the real data, we found that the error between them is small and the goodness of fit can reach 0.988. Therefore, LSTM can be used to predict the trading trend of the meta-world in NFT.