Метод згладженої автокореляційної функції для прогнозування варіації гетероскедастичних часових рядів
The paper proposes a new method for forecasting the variability for strong volatile heteroscedastic time series. An autoregressive model of an infinite order is considered as a model of time series. Parameters of the model are found as a solution of a Toeplitz system that uses correlation coefficien...
Збережено в:
| Видавець: | The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" |
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| Дата: | 2015 |
| Автор: | |
| Формат: | Стаття |
| Мова: | Ukrainian |
| Опубліковано: |
The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
2015
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| Онлайн доступ: | http://journal.iasa.kpi.ua/article/view/54473 |
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Репозиторії
System research and information technologies| Резюме: | The paper proposes a new method for forecasting the variability for strong volatile heteroscedastic time series. An autoregressive model of an infinite order is considered as a model of time series. Parameters of the model are found as a solution of a Toeplitz system that uses correlation coefficients. The model of the autocorrelation function at every forecasting step is constructed by solving an optimization problem that takes into account the condition of strong dependence. The method has been tested on artificially generated and real time series. The autoregressive model parameters found with the method of maximum likelihood were used to compare the results of a selected autoregressive model. The results show a substantially high effectiveness of the proposed method in predicting of strong volatile heteroscedastic time series. |
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