Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | Экспоненциальный GARCH (EGARCH)× | Простое и двойное экспоненциальное сглаживание (SES / Холт)× | |
|---|---|---|---|
| Область | Эконометрика | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model | Regression model |
| Год появления≠ | 2015 | 1991 | 1957 |
| Автор метода≠ | Box & Jenkins (Box-Jenkins methodology) | Nelson | Robert G. Brown (SES); Charles C. Holt (linear trend) |
| Тип≠ | Univariate time-series model | Conditional volatility model (asymmetric GARCH variant) | Exponential smoothing forecasting model |
| Основополагающий источник≠ | 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 | Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. DOI ↗ | Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. link ↗ |
| Другие названия≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH | SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt) |
| Связанные≠ | 5 | 4 | 3 |
| Сводка≠ | 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). | EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance. | Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta. |
| ScholarGateНабор данных ↗ |
|
|
|