Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель Фурье SARIMA× | Модель SARIMA со структурными разрывами× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1994 | 1970s–1998 |
| Автор метода≠ | Harvey & Scott (1994); Hyndman & Athanasopoulos (popularization) | Box & Jenkins (SARIMA); Bai & Perron (structural break detection) |
| Тип≠ | Seasonal time series model with trigonometric regressors | Time series model with regime shifts |
| Основополагающий источник≠ | Harvey, A., & Scott, A. (1994). Seasonality in dynamic regression models. The Economic Journal, 104(427), 1324-1345. link ↗ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ |
| Другие названия | Fourier SARIMA, SARIMA with Fourier terms, Fourier-SARIMA, trigonometric SARIMA | SARIMA with structural breaks, break-augmented SARIMA, piecewise SARIMA, SARIMA-SB |
| Связанные≠ | 6 | 3 |
| Сводка≠ | The Fourier SARIMA model extends the classical Seasonal ARIMA framework by incorporating trigonometric (Fourier) terms as deterministic regressors. This allows the model to approximate smooth, complex, or multiple-frequency seasonal patterns without requiring a full seasonal ARIMA structure for every frequency, making it particularly useful for high-frequency data or series with non-integer or evolving seasonality. | The Structural Break SARIMA model extends the classical Seasonal ARIMA framework by explicitly detecting and accommodating abrupt, permanent shifts in the level, trend, or seasonal pattern of a time series. Rather than forcing a single SARIMA specification across the entire sample, the model partitions the series at estimated breakpoints and fits separate SARIMA processes to each resulting segment, producing more accurate forecasts and reliable inference in the presence of regime changes. |
| ScholarGateНабор данных ↗ |
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