Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Prophet× | SARIMAX× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2018 | 2015 |
| Autor original≠ | Taylor & Letham (Facebook/Meta) | Box & Jenkins (ARIMA framework); SARIMAX extension with exogenous regressors |
| Tipo≠ | Decomposable (structural) time series model | Seasonal time-series regression model |
| Fonte seminal≠ | Taylor, S. J. & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45. DOI ↗ | Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts. link ↗ |
| Outros nomes≠ | Prophet, Facebook Prophet, Meta Prophet, forecasting at scale | seasonal ARIMA with exogenous variables, SARIMA with regressors, ARIMAX, SARIMAX — Dışsal Değişkenli Mevsimsel ARIMA |
| Relacionados≠ | 5 | 4 |
| Resumo≠ | Prophet is a Bayesian structural time series model introduced by Taylor and Letham at Facebook/Meta in 2018. It forecasts a continuous series by decomposing it into separate, interpretable trend, seasonality, and holiday components, and is designed to be approachable for analysts working at scale. | SARIMAX extends the seasonal ARIMA (Box-Jenkins) model by adding exogenous explanatory variables, so it can capture the effect of holidays, economic indicators, or policy variables on a time series. It combines non-seasonal and seasonal autoregressive and moving-average dynamics with external regressors, and is estimated by maximum likelihood in state-space form. |
| ScholarGateConjunto de dados ↗ |
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