Forecasting trends in the cryptocurrency exchange rate through the machine learning theory

Abstract

Subject. The study discusses methodological approaches to forecasting trends in the development of the cryptocurrency market (bitcoin). Objectives. The study aims to discover and explain tools and mechanisms for predicting how the cyptocurrency market may evolve in a short run through time series modeling methods and machine learning methods, which are based on artificial neural networks LSTM. Methods. Using Python-based programming methods, we constructed and substantiated a neural network model for the analyzable series describing how the stock exchange rate of bitcoin develops. Results. Matching loss functions, optimizer and parameters for constructing a neural network that predicts the BTC/USD exchange rate for a coming day, we proved its applicability and feasibility, which is confirmed with the lowest number of errors in the test and validation set. Conclusions and Relevance. The findings mainly prove that the above mechanism is feasible for predicting the cryptocurrency market. The mechanism is based on algorithms for constructing LSTM networks. The approach should be used to analyze and evaluate the current and future parameters of the cryptocurrency market development. The tools can be of interest for investors which operate in new markets of e-money.