Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Model priestorového stavu (Kalmanov filter)× | Štruktúrny časový radový model (Základný štruktúrny model)× | |
|---|---|---|
| Odbor | Ekonometria | Ekonometria |
| Rodina | Regression model | Regression model |
| Rok vzniku | 1990 | 1990 |
| Tvorca≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Andrew C. Harvey |
| Typ≠ | State space time series model | State-space (unobserved components) time series model |
| Pôvodný zdroj | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 |
| Ďalšie názvy | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| Príbuzné | 4 | 4 |
| Zhrnutie≠ | 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. | The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit. |
| ScholarGateDátová sada ↗ |
|
|