Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Netezire Exponențială Simplă și Dublă (SES / Holt)× | Modelul spațiului de stare (Filtrul Kalman)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1957 | 1990 |
| Autorul original≠ | Robert G. Brown (SES); Charles C. Holt (linear trend) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Tip≠ | Exponential smoothing forecasting model | State space time series model |
| Sursa seminală≠ | Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. link ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Denumiri alternative | SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt) | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Înrudite≠ | 3 | 4 |
| Rezumat≠ | Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta. | 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. |
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