Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Теория экстремальных значений (Extreme Value Theory, EVT)× | Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | |
|---|---|---|
| Область≠ | Финансы | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2001 | 2015 |
| Автор метода≠ | Coles (textbook treatment); McNeil, Frey & Embrechts | Box & Jenkins (Box-Jenkins methodology) |
| Тип≠ | Tail / extreme-event model | Univariate time-series model |
| Основополагающий источник≠ | Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598 | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| Другие названия≠ | EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Связанные | 5 | 5 |
| Сводка≠ | Extreme Value Theory is a statistical framework for modelling the rare events that live in the tail of a probability distribution. As developed in Coles (2001) and applied to risk by McNeil, Frey & Embrechts (2005), it offers two standard routes: the Generalized Extreme Value (GEV) distribution for block maxima and the Generalized Pareto Distribution (GPD), used in the peaks-over-threshold approach, for exceedances above a high threshold. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
| ScholarGateНабор данных ↗ |
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