Сравнение методов

	Робастная модель ARIMA	Модель пространства состояний (фильтр Калмана)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1986–1993	1990
Автор метода	Tsay (1986); Chen & Liu (1993)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Тип	Robust time series model	State space time series model
Основополагающий источник	Tsay, R. S. (1986). Time series model specification in the presence of outliers. Journal of the American Statistical Association, 81(393), 132–141.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Другие названия	robust ARIMA, outlier-resistant ARIMA, robust time series estimation, ARIMA with outlier detection	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Связанные	4	4
Сводка	Robust ARIMA extends the classical ARIMA framework to detect and correct the influence of outliers and structural breaks during estimation. By jointly identifying anomalous observations and re-estimating model parameters, it produces coefficient estimates and forecasts that are far less distorted by isolated shocks or data errors than standard ARIMA.	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.