Using R/S analysis for forecasting stock quotes with ARMA and ARIMA methods
The article observes the methods of forecasting time series Autoregressive Moving Average Model (ARMA) and Integrated Autoregressive Moving Average Model (ARIMA). The ARIMA model differs from the ARMA model only in that forecasting is performed not on absolute values of series levels, but on differe...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/03/itmconf_hmmocs-III2024_04009.pdf |
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| Summary: | The article observes the methods of forecasting time series Autoregressive Moving Average Model (ARMA) and Integrated Autoregressive Moving Average Model (ARIMA). The ARIMA model differs from the ARMA model only in that forecasting is performed not on absolute values of series levels, but on differences of order
d, which makes it possible to apply to non-stationary time series. Financial time series are traditionally considered non-stationary. However, the Hurst exponent less than or equal to 0.5 indicates a random or anti-persistent nature of time series. The paper assumes that for random or anti-persistent time series according to Hurst, there is no need to take differences and it is sufficient to apply the ARMA model for forecasting. To test the hypothesis, we carried out forecasts of leading world indices stocks and currency pairs with the Hurst exponent less than or equal to 0.5 for 10 years using the ARMA and ARIMA methods and compared the results using the MAPE metric. According to the ARMA method forecasts, in most cases the error was smaller, that confirmed the initial hypothesis. |
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| ISSN: | 2271-2097 |