Approaches for Cryptocurrency Price Prediction

Abstract

This paper describes approaches to forecast Ethereum price based on regression analysis which are based on defined in this research list of factors which may affect price. These parameters can be a part of fundamental and technical analysis. In scope of forecasting the nonlinear regression models are used and compared, in couple with prediction of each factor which is used for regression by NeuralProphet. The models’ outputs were retrieved during experiment. Also, experiment includes models tuning to have more accurate result. The data time window for experiment is one year. This paper does not consider influence of political situation and nature cataclysms on cryptocurrency. Also, this research does not include index of openness of countries finance institute. The type of analyzed crypto is decentralized finance. The Java microbenchmark harness is used to calculate time which is spent for models training. Models’ performance is calculated by evaluation of regression metrics: Root mean square error, Mean absolute error, Mean square error, Explained variance.