方法对比
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| ARIMA（自回归积分滑动平均）模型× | 高斯、t、Clayton、Gumbel、Frank 联结模型× | |
|---|---|---|
| 领域≠ | 计量经济学 | 金融学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2015 | 1959 |
| 提出者≠ | Box & Jenkins (Box-Jenkins methodology) | Sklar (1959); dependence-concept treatment by Joe (1997) |
| 类型≠ | Univariate time-series model | Dependence model |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | copulas, dependence copulas, vine copulas, Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank) |
| 相关 | 5 | 5 |
| 摘要≠ | 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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