Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle ARIMA (Autoregressive Integrated Moving Average)× | Modèles de copules (Gaussienne, t, Clayton, Gumbel, Frank)× | |
|---|---|---|
| Domaine≠ | Économétrie | Finance |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2015 | 1959 |
| Auteur d'origine≠ | Box & Jenkins (Box-Jenkins methodology) | Sklar (1959); dependence-concept treatment by Joe (1997) |
| Type≠ | Univariate time-series model | Dependence model |
| Source fondatrice≠ | 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 | Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut Statistique de l'Université de Paris, 8, 229-231. link ↗ |
| Alias≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | copulas, dependence copulas, vine copulas, Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank) |
| Apparentées | 5 | 5 |
| Résumé≠ | 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). | Copula models are a family of functions that describe the dependence structure between variables separately from their individual (marginal) distributions. The foundation is Sklar's theorem (1959), which shows that any multivariate distribution can be split into its marginals plus a copula; Joe (1997) developed the modern catalogue of dependence concepts. They are central to portfolio risk and credit modelling. |
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