Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo ARIMA (Autoregressive Integrated Moving Average)× | DCC-GARCH (Correlação Condicional Dinâmica)× | Suavização Exponencial Simples e Dupla (SES / Holt)× | |
|---|---|---|---|
| Área≠ | Econometria | Finanças | Econometria |
| Família | Regression model | Regression model | Regression model |
| Ano de origem≠ | 2015 | 2002 | 1957 |
| Autor original≠ | Box & Jenkins (Box-Jenkins methodology) | Robert F. Engle | Robert G. Brown (SES); Charles C. Holt (linear trend) |
| Tipo≠ | Univariate time-series model | Multivariate volatility model | Exponential smoothing forecasting model |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes≠ | 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) |
| Relacionados≠ | 5 | 5 | 3 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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