Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Furjē slīdošā vidējā (Furjē MA) modelis× | Furjē ARIMA modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1990s–2000s | 2004-2012 |
| Autors≠ | Harvey, A. C.; Hyndman, R. J. | Becker, Enders, and Hurn; further extended by Enders and Lee |
| Tips | Time series model | Time series model |
| Pirmavots≠ | Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts. link ↗ | Enders, W., & Lee, J. (2012). The flexible Fourier form and Dickey-Fuller type unit root tests. Economics Letters, 117(1), 196-202. DOI ↗ |
| Citi nosaukumi | Fourier MA, Fourier-augmented moving average, trigonometric MA model, harmonic moving average model | Fourier ARIMA, ARIMA with Fourier terms, trigonometric ARIMA, Fourier-flexible ARIMA |
| Saistītās | 2 | 2 |
| Kopsavilkums≠ | 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 Fourier ARIMA model augments a standard ARIMA specification with trigonometric sine and cosine terms, allowing it to capture smooth, gradual structural change and flexible nonlinear seasonality without specifying the exact timing or number of breaks in advance. It is widely used in applied macroeconometrics and finance for series exhibiting slowly evolving dynamics. |
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