Сравнение на методи

	ARFIMA: Модел с дробно интегрирани ARMA	Регресия с гребен (Ridge Regression)
Област	Иконометрия	Машинно обучение
Семейство	Regression model	Machine learning
Година на възникване	1980	1970
Създател	Granger & Joyeux (1980); Hosking (1981)	Hoerl, A.E. & Kennard, R.W.
Тип	Long-memory time series model	L2-regularized linear regression
Основополагащ източник	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.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Други названия	fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Свързани	5	4
Резюме	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.