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Blockchain technology shows significant results and huge potential forserving as an interweaving fabric that goes through every industry and market,allowing decentralized and secure value exchange, thus connecting ourcivilization like never before. The standard approach for asset valuepredictions is based on market analysis with an LSTM neural network. Blockchaintechnologies, however, give us access to vast amounts of public data, such asthe executed transactions and the account balance distribution. We explorewhether analyzing this data with modern Deep Leaning techniques results inhigher accuracies than the standard approach. During a series of experiments onthe Ethereum blockchain, we achieved $4$ times error reduction with blockchaindata than an LSTM approach with trade volume data. By utilizing blockchainaccount distribution histograms, spatial dataset modeling, and a Convolutionalarchitecture, the error was reduced further by 26\%. The proposed methodologiesare implemented in an open source cryptocurrency prediction framework, allowingthem to be used in other analysis contexts.

Predicting digital asset market based on blockchain activity data