Financial time series forecasting methods
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 t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2024/02/itmconf_hmmocs2023_02005.pdf |
| Summary: | 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. |
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| ISSN: | 2271-2097 |