השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מודל מרחב מצב (מסנן קלמן)× | מודל ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| תחום | אקונומטריקה | אקונומטריקה |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 1990 | 2015 |
| הוגה השיטה≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Box & Jenkins (Box-Jenkins methodology) |
| סוג≠ | State space time series model | Univariate time-series model |
| מקור מכונן≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| כינויים≠ | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| קשורות≠ | 4 | 5 |
| תקציר≠ | 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. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
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