The neural networks are tool for approximation universal series of data, but their precision highly depends on a adequate set of inputs. Cryptocurrencies experience high levels of volatility due to absence of agreed upon pricing methodologies behind its valuation. In this article we analyze approaches of obtaining additional inputs for neural networks and explore their influence on its precision.
