title: Ð¡ÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÐºÑÑÑÐ° ÐºÑÐ¸Ð¿ÑÐ¾Ð²Ð°Ð»ÑÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð±Ð¸ÑÐºÐ¾Ð¸Ð½Ð°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Comparative analysis of cryptocurrency rate prediction models based on bitcoin.
Creators: name: ÐÐ°ÑÑÐ¸ÑÐ½ÑÐ¹, ÐÐ»Ð°Ð´Ð¸ÑÐ»Ð°Ð²
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: Ð±Ð¸ÑÐºÐ¾Ð¸Ð½
Ð±Ð»Ð¾ÐºÑÐµÐ¹Ð½
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
bitcoin
blockchain
machine learning

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«Ð¡ÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÐºÑÑÑÐ° ÐºÑÐ¸Ð¿ÑÐ¾Ð²Ð°Ð»ÑÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð±Ð¸ÑÐºÐ¾Ð¸Ð½Ð°Â». Ð¦ÐµÐ»ÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÑÐ°Ð»Ð¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐµÐ¹ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¸ Ð¸Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð², Ð¾ÑÑÐ°Ð¶Ð°ÑÑÐ¸Ñ Ð¸Ð·Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÐºÑÑÑÐ° ÐºÑÐ¸Ð¿ÑÐ¾Ð²Ð°Ð»ÑÑÑ. ÐÐ°Ð´Ð°ÑÐ¸, ÑÐµÑÐµÐ½Ð½ÑÐµ Ð² ÑÐ°Ð±Ð¾ÑÐµ:1. ÐÐ·ÑÑÐµÐ½Ð¸Ðµ Ð¸ÑÑÐ¾ÑÐ¸Ð¸ Ð¸ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐµÐ¹ ÐºÑÐ¸Ð¿ÑÐ¾Ð²Ð°Ð»ÑÑ, Ð² ÑÐ¾Ð¼ ÑÐ¸ÑÐ»Ðµ ÑÐ°ÐºÐ¾Ð¹ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½Ð¾Ð¹ Ð² Ð¼Ð¸ÑÐµ ÐºÑÐ¸Ð¿ÑÐ¾Ð²Ð°Ð»ÑÑÑ ÐºÐ°Ðº Ð±Ð¸ÑÐºÐ¾Ð¸Ð½.2. ÐÐ·ÑÑÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð².3. ÐÑÐ±Ð¾Ñ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð² Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÐºÑÑÑÐ° ÐºÑÐ¸Ð¿ÑÐ¾Ð²Ð°Ð»ÑÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð±Ð¸ÑÐºÐ¾Ð¸Ð½Ð° Ð´Ð»Ñ Ð¸Ñ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐµÐ³Ð¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ.4. Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð²ÑÐ±ÑÐ°Ð½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÐºÑÑÑÐ° Ð±Ð¸ÑÐºÐ¾Ð¸Ð½Ð° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ·ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÐ¹ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÐ¹ ÐºÐ¾Ð´ Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ð» Ð²ÑÐ¿Ð¾Ð»Ð½Ð¸ÑÑ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð²ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹: ARIMA Ð¸ LSTM.
description: The theme of the final qualification work: âComparative analysis of cryptocurrency rate prediction models based on bitcoinâ. The goal of this graduate thesis is to investigate whether machine learning models offer better predictions than traditional models in terms of lower prediction errors and higher prediction accuracy. The tasks solved in the course of the work:1. Studying the history and characteristics of cryptocurrencies, including such a popular cryptocurrency in the world as bitcoin.2. To study different machine learning models for predicting time series.3. Selecting the most appropriate time series models for predicting bitcoin-based cryptocurrency rates for further research.4. Implementation of the selected models for bitcoin rate prediction using Python programming language. The developed program code allowed performing a comparative analysis of two models: ARIMA and LSTM.