Decentralized Storage Cryptocurrencies: An Innovative Network-Based Model for Identifying Effective Entities and Forecasting Future Price Trends

Abstract

Abstract This study focuses on analyzing four of the most significant cryptocurrencies in the field of decentralized storage, including Filecoin, Arweave, Storj, and Siacoin. Our method consists of three main components: Network Analysis, Textual Analysis, and Market Analysis. Network Analysis involves identifying relevant entities associated with the target cryptocurrencies to construct a network of entities. During this component, the embeddings of each entity are then extracted using node2vec which are fed into a convolutional neural network. In the second component, Textual Analysis, we first employ the T5 summarization model to encapsulate the content of related news articles. Subsequently, by utilizing the FinBert model the sentiment of news articles and tweets associated with the identified entities are extracted. We then use transformer encoders to process the resulting feature vectors. Ultimately, similar to the Textual component, by leveraging the transformer encoders the financial market information of target cryptocurrencies is evaluated during the Market Analysis component. As the final step, the outputs of these components are combined to predict the price trend of the target cryptocurrencies within a specified time frame. The proposed model’s accuracy in forecasting the future price trend of Filecoin, Storj, Arweave, and Siacoin is 76%, 83%, 61%, and 74% respectively.