Сравнение на методи

	Time-varying parameter ARIMA model	Модел ARIMA (Авторегресионен интегриран плъзгащ се среден)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1976–1989	1970
Създател	Cooley & Prescott (1976); Harvey (1989) state-space formulation	George Box and Gwilym Jenkins
Тип	Time series model with evolving coefficients	Time series forecasting model
Основополагащ източник	Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521405737	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Други названия	TVP-ARIMA, time-varying ARIMA, adaptive ARIMA, state-space ARIMA	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Свързани	3	6
Резюме	The time-varying parameter ARIMA model extends the classical ARIMA framework by allowing its autoregressive and moving-average coefficients to evolve over time rather than remaining fixed. Cast in state-space form and estimated via the Kalman filter, it is designed for economic and financial time series whose dynamic structure shifts in response to structural breaks, policy changes, or regime transitions.	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.