Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-II-2023); Krasnoyarsk; Krasnoyarsk

Год издания: 2024

Идентификатор DOI: 10.1051/itmconf/20245902005

Аннотация: The paper presents the development of time series forecasting algorithms based on the Integrated Autoregressive Moving Average Model (ARIMA) and the Fourier Expansion model. These models were applied to non-stationary time series of stock quotes after bringing these series to a stationary form. In the paper, ARIMA and Fourier Expansion model were constructed, using Python development environment. The developed algorithms were tested on Russian and American stock indices using the Mean Absolute Percentage Error metric.

Журнал: Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-II-2023)

Номера страниц: 2005

Место издания: Krasnoyarsk

• Zinenko Anna (Siberian Federal University)
• Stupina Alena (Siberian Federal University)

• РИНЦ (eLIBRARY.RU)