Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Kielelezo cha SARIMA chenye Vigezo Vinavyobadilika kwa Wakati (TVP-SARIMA)× | Mfumo wa ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1990s | 1970 |
| Mwanzilishi≠ | Harvey, A. C.; Durbin, J. & Koopman, S. J. (state-space framework) | George Box and Gwilym Jenkins |
| Aina≠ | Time-varying state-space model | Time series forecasting model |
| Chanzo asilia≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 9780521321969 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Majina mbadala | TVP-SARIMA, time-varying SARIMA, state-space SARIMA, adaptive SARIMA | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Zinazohusiana≠ | 4 | 6 |
| Muhtasari≠ | The Time-Varying Parameter SARIMA model extends the classical SARIMA framework by allowing autoregressive and moving-average coefficients to evolve over time. Cast as a state-space system and estimated with the Kalman filter, it captures both seasonal patterns and structural change within a single unified model. | 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. |
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