Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo de Média Móvel de Fourier (Fourier MA)× | Modelo ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1990s–2000s | 1970 |
| Autor original≠ | Harvey, A. C.; Hyndman, R. J. | George Box and Gwilym Jenkins |
| Tipo≠ | Time series model | Time series forecasting model |
| Fonte seminal≠ | Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts. link ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Outros nomes | Fourier MA, Fourier-augmented moving average, trigonometric MA model, harmonic moving average model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Relacionados≠ | 2 | 6 |
| Resumo≠ | The Fourier MA model combines a Moving Average (MA) error structure with Fourier series terms — sine and cosine pairs — to capture complex or high-frequency seasonal patterns in time series data. It is particularly useful when the seasonal period is long or irregular, making classical seasonal ARIMA parameterisation infeasible. | The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
| ScholarGateConjunto de dados ↗ |
|
|