title: ÐÐµÑÐ¾Ð´Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¸Ð½Ð°Ð½ÑÐ¾Ð²ÑÑ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð²
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: Methods for forecasting financial time series
Creators: name: Ð¢Ð¸Ð¼Ð°ÐºÐ¾Ð², ÐÐ¸ÑÐ°Ð»Ð¸Ð¹
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: Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð¹ ÑÑÐ´
Ð°Ð½Ð°Ð»Ð¸Ð·
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÑÐ°ÑÐ¸ÑÑÐ¸ÐºÐ°
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ARIMA
RNN
LSTM
time series
analysis
predictive model
machine learning
statistic
neural network

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÐµÑÐ¾Ð´Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¸Ð½Ð°Ð½ÑÐ¾Ð²ÑÑ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð²Â». Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐµÑÐ°ÐµÑÑÑ Ð¿ÑÐ¸ÐºÐ»Ð°Ð´Ð½Ð°Ñ Ð·Ð°Ð´Ð°ÑÐ° Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¸Ð½Ð°Ð½ÑÐ¾Ð²Ð¾Ð³Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÑÐ´Ð° ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð¹ Ð²Ð°Ð»ÑÑÑ. Ð Ð°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÑÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ARIMAÂ Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ LSTMÂ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ ÑÐµÐºÑÑÑÐµÐ½ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÐ°Ð´Ð°ÑÐ¸, ÑÐµÑÐ°ÐµÐ¼ÑÐµ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: ÐÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¸ Ð¼ÐµÑÐ¾Ð´Ñ, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼ÑÐµ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¸Ð½Ð°Ð½ÑÐ¾Ð²ÑÑ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð². ÐÑÐ±ÑÐ°ÑÑ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¸Ð½Ð°Ð½ÑÐ¾Ð²ÑÑ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð². ÐÐ¾Ð´Ð³Ð¾ÑÐ¾Ð²Ð¸ÑÑ Ð¸ÑÑÐ¾Ð´Ð½ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ Ð¿Ð¾Ð´ ÑÑÐµÐ±Ð¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐ±ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°. Ð¡Ð´ÐµÐ»Ð°ÑÑ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð²ÑÐ±ÑÐ°Ð½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹. ÐÐ¾ÑÑÑÐ¾Ð¸ÑÑ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð· ÑÐ°Ð·Ð²Ð¸ÑÐ¸Ñ Ð¸ÑÑÐ¾Ð´Ð½Ð¾Ð³Ð¾ ÑÑÐ´Ð° Ð½Ð° 7 Ð´Ð½ÐµÐ¹. ÐÑÐ¾Ð²ÐµÑÑÐ¸ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸ ÑÐ´ÐµÐ»Ð°ÑÑ Ð²ÑÐ²Ð¾Ð´ Ð¾ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð½Ð° Ð¿ÑÐ°ÐºÑÐ¸ÐºÐµ. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½ Ð¾Ð±Ð·Ð¾Ñ Ð¾Ð±ÑÐµÐ³Ð¾ ÐºÐ»Ð°ÑÑÐ° Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ Ð´Ð»Ñ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ñ ÑÑÐ´Ð¾Ð² Ð¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð°, Ð° ÑÐ°ÐºÐ¶Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð±ÑÐ» Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½ Ð¸ÑÑÐ¾Ð´Ð½ÑÐ¹ Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð¹ ÑÑÐ´ ÑÐµÐ½Ñ Ð·Ð°ÐºÑÑÑÐ¸Ñ ÐºÑÐ¸Ð¿ÑÐ¾Ð²Ð°Ð»ÑÑÑ Ethereum. ÐÐ¾ÑÑÑÐ¾ÐµÐ½Ð¾ Ð´Ð²Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸: ARIMAÂ Ð¸ LSTM. Ð Ð¿Ð¾Ð»ÑÑÐµÐ½ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð· ÑÐ°Ð·Ð²Ð¸ÑÐ¸Ñ ÑÑÐ´Ð° Ð½Ð° 7 Ð´Ð½ÐµÐ¹ Ð¾Ñ ÐºÐ°Ð¶Ð´Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸. ÐÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð», ÑÑÐ¾ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ñ Ð·Ð°Ð´Ð°ÑÐµÐ¹ ÑÐ¿ÑÐ°Ð²Ð¸Ð»Ð°ÑÑ Ð»ÑÑÑÐµ Ð½ÐµÐ¶ÐµÐ»Ð¸ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ.Â Â Â Â Â ÐÐ¸ Ð¾Ð´Ð½Ð° Ð¸Ð· Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð² Ð¿Ð¾Ð»Ð½Ð¾Ð¹ Ð¼ÐµÑÐµ Ð½Ðµ ÑÐ¼Ð¾Ð³Ð»Ð° Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°ÑÑ Ð²ÑÐ±ÑÐ¾ÑÑ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ ÑÑÐ´Ð°, Ð½Ð¾ Ð¿ÑÐ¸ ÑÑÐ¾Ð¼ ÑÐµÐ½Ð´ÐµÐ½ÑÐ¸Ñ ÑÐ°Ð·Ð²Ð¸ÑÐ¸Ñ ÑÑÐ´Ð° Ð¸ ÐºÐ¾Ð½ÐµÑÐ½ÑÐµ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ Ð±ÑÐ»Ð¸ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ñ Ð´Ð¾ÑÑÐ°ÑÐ¾ÑÐ½Ð¾ ÑÐ¾ÑÐ½Ð¾. Ð¡ÑÐµÐ´Ð½ÑÑ Ð°Ð±ÑÐ¾Ð»ÑÑÐ½Ð°Ñ Ð¾ÑÐ¸Ð±ÐºÐ° Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¾Ð² Ð¼Ð¾Ð´ÐµÐ»Ð¸Â ARIMA Ð½Ð° ÑÐµÑÑÐ¾Ð²Ð¾Ð¹ Ð²ÑÐ±Ð¾ÑÐºÐµ ÑÐ¾ÑÑÐ°Ð²Ð¸Ð»Ð° 80,87. Ð¡ÑÐµÐ´Ð½ÑÑ Ð°Ð±ÑÐ¾Ð»ÑÑÐ½Ð°Ñ Ð¾ÑÐ¸Ð±ÐºÐ° Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¾Ð² Ð¼Ð¾Ð´ÐµÐ»Ð¸ LSTMÂ Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ ÑÐµÑÑÐ¾Ð²Ð¾Ð¹ Ð²ÑÐ±Ð¾ÑÐºÐ¾Ð¹ ÑÐ¾ÑÑÐ°Ð²Ð¸Ð»Ð° 75,27. ÐÐ¾ÑÑÑÐ¸ÑÐ¸ÐµÐ½Ñ Ð´ÐµÑÐµÑÐ¼Ð¸Ð½Ð°ÑÐ¸Ð¸ ÑÐ°Ð²ÐµÐ½ 0,856, ÑÑÐ¾ Ð³Ð¾Ð²Ð¾ÑÐ¸Ñ Ð¾ Ð´Ð¾ÑÑÐ°ÑÐ¾ÑÐ½Ð¾ Ð²ÑÑÐ¾ÐºÐ¾Ð¹ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸.
description: The theme of the final qualification work: "Methods for forecasting financial time series". In this paper, the applied problem of forecasting the financial time series of digital currency is solved. The classical ARIMA analysis and forecasting model and the LSTM deep learning model of the recurrent neural network architecture are considered. Tasks to be solved in the course of the study: Analyze existing algorithms and methods used for forecasting financial time series. Choose the best methods for forecasting financial time series. Prepare the initial data for the requirements of the selected algorithm. Make the implementation of the selected models. Build a forecast for the development of the initial series for 7 days. Analyze the results of forecasting and draw a conclusion about the possibility of applying the models in practice. The paper presents an overview of the general class of models that can be used to describe series and build a forecast, as well as a machine learning algorithm. As a result, the initial time series of the closing price of the Ethereum cryptocurrency was analyzed. Two models have been built: ARIMA and LSTM. And a forecast for the development of a series for 7 days was obtained from each model. A comparative analysis of the results of the models was carried out, which showed that the neural network coped with the task better than the statistical model. None of the models was able to fully predict the outliers in the values of the series, but the trend in the development of the series and the final values were determined quite accurately. The average absolute error of the ARIMA model forecasts on the test sample was 80,87. The average absolute forecast error of the LSTM model compared to the test sample was 75,27. The coefficient of determination is 0,856, which indicates a fairly high accuracy of the model's predictions.