Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| ARFIMA: Модель дробово інтегрованої ARMA× | Гребенева регресія× | |
|---|---|---|
| Галузь≠ | Економетрика | Машинне навчання |
| Родина≠ | Regression model | Machine learning |
| Рік появи≠ | 1980 | 1970 |
| Автор методу≠ | Granger & Joyeux (1980); Hosking (1981) | Hoerl, A.E. & Kennard, R.W. |
| Тип≠ | Long-memory time series model | L2-regularized linear regression |
| Основоположне джерело≠ | Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15–29. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Інші назви≠ | fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Пов'язані≠ | 5 | 4 |
| Підсумок≠ | ARFIMA is a time series model that captures long-memory behaviour using a fractional differencing parameter d, generalising the integer differencing of ARIMA. It was introduced by Granger and Joyeux (1980) and formalised by Hosking (1981) to describe series whose autocorrelations decay slowly rather than abruptly. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
| ScholarGateНабір даних ↗ |
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