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 |
| _version_ | 1797333952371884032 |
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| author | Zinenko Anna Stupina Alena |
| author_facet | Zinenko Anna Stupina Alena |
| author_sort | Zinenko Anna |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T08:13:38Z |
| format | Article |
| id | doaj.art-8fbc6c628326414184476ad1e0da39aa |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-08T08:13:38Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-8fbc6c628326414184476ad1e0da39aa2024-02-02T08:04:05ZengEDP SciencesITM Web of Conferences2271-20972024-01-01590200510.1051/itmconf/20245902005itmconf_hmmocs2023_02005Financial time series forecasting methodsZinenko Anna0Stupina Alena1Siberian Federal UniversitySiberian Federal UniversityThe 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.https://www.itm-conferences.org/articles/itmconf/pdf/2024/02/itmconf_hmmocs2023_02005.pdf |
| spellingShingle | Zinenko Anna Stupina Alena Financial time series forecasting methods ITM Web of Conferences |
| title | Financial time series forecasting methods |
| title_full | Financial time series forecasting methods |
| title_fullStr | Financial time series forecasting methods |
| title_full_unstemmed | Financial time series forecasting methods |
| title_short | Financial time series forecasting methods |
| title_sort | financial time series forecasting methods |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2024/02/itmconf_hmmocs2023_02005.pdf |
| work_keys_str_mv | AT zinenkoanna financialtimeseriesforecastingmethods AT stupinaalena financialtimeseriesforecastingmethods |