Salīdzināt metodes

	ARFIMA: Daļēji integrēts ARMA modelis	Regulētā lineārā regresija (Ridge Regression)
Nozare	Ekonometrija	Mašīnmācīšanās
Saime	Regression model	Machine learning
Izcelsmes gads	1980	1970
Autors	Granger & Joyeux (1980); Hosking (1981)	Hoerl, A.E. & Kennard, R.W.
Tips	Long-memory time series model	L2-regularized linear regression
Pirmavots	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.
Citi nosaukumi	fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Saistītās	5	4
Kopsavilkums	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.