title: ÐÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÐµÐ½Ð´Ð° ÐºÐ¾ÑÐ¸ÑÐ¾Ð²Ð¾Ðº ÑÐ¸Ð½Ð°Ð½ÑÐ¾Ð²ÑÑ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ¾Ð² Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Forecasting quotes trend of the financial instruments using machine learning
Creators: name: ÐÐ¾ÑÐ¾ÑÐ¸Ð»Ð¾Ð², ÐÐ°ÐºÑÐ¸Ð¼
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: 2021
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÐºÐ¾ÑÐ¸ÑÐ¾Ð²ÐºÐ¸
Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÐ°Ð·Ð»Ð¾Ð¶Ð¸Ð¼ÑÑ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð²
ÑÐ¾Ð½Ð´Ð¾Ð²ÑÐ¹ ÑÑÐ½Ð¾Ðº
machine learning
prediction
quotes
decomposable time series model
stock market

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description: In this study different approaches to predicting the stock market were considered. The paper demonstrates a review of existing solutions based on the analysis of scientific articles. The purpose of the graduation work is to develop a system for predicting the closing price trend of the stocks of technology companies. The basis of the implemented approach is the decomposable time series model with three main components: trend, seasonality, and holidays. This study assumes that it is advisable to predict the stocks of technology companies by simulating seasonal changes. The forecasting system is implemented using Python programming language and Facebook Prophet library with set of additional tools. The implemented model has demonstrated high prediction accuracy on the selected datasets. In each of the experiments, the model correctly predicted the future trend of the financial time series. This made it possible to confirm the assumption about the advisability of predicting the stocks of technology companies by introducing custom seasonality, which is adapted to the specifics of each time series.