| Issue |
ITM Web Conf.
Volume 59, 2024
II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
|
|
|---|---|---|
| Article Number | 02005 | |
| Number of page(s) | 7 | |
| Section | Interdisciplinary Mathematical Modeling and Applications | |
| DOI | https://doi.org/10.1051/itmconf/20245902005 | |
| Published online | 25 January 2024 | |
Financial time series forecasting methods
Siberian Federal University,
79, Svobodny Prospect,
Krasnoyarsk,
660049,
Russian Federation
* Corresponding author: anna-z@mail.ru
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.
© The Authors, published by EDP Sciences, 2024
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