Impact Analysis of Additional Input Parameters on Neural Network Cryptocurrency Price Prediction

Abstract

Neural network is the universal approximator, but its precision highly depends on sufficient set of inputs. Cryptocurrencies have great volatility, due to absence of fundamentals to back up their price. In this paper we analyze approaches to obtain additional parameters for neural networks and explore their impact on its prediction accuracy. This study indicates significant improvement of neural network predictions due to inclusion of wider selection of relevant data points.