Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель ARIMA (Авторегресійна інтегрована ковзна середня)× | DCC-GARCH (Dynamic Conditional Correlation)× | Просте та подвійне експоненційне згладжування (SES / Хольт)× | |
|---|---|---|---|
| Галузь≠ | Економетрика | Фінанси | Економетрика |
| Родина | Regression model | Regression model | Regression model |
| Рік появи≠ | 2015 | 2002 | 1957 |
| Автор методу≠ | Box & Jenkins (Box-Jenkins methodology) | Robert F. Engle | Robert G. Brown (SES); Charles C. Holt (linear trend) |
| Тип≠ | Univariate time-series model | Multivariate volatility model | Exponential smoothing forecasting model |
| Основоположне джерело≠ | 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 | Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350. DOI ↗ | Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. link ↗ |
| Інші назви≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon | SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt) |
| Пов'язані≠ | 5 | 5 | 3 |
| Підсумок≠ | 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). | DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step. | 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. |
| ScholarGateНабір даних ↗ |
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