Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Strukturālais laika sēriju modelis (Pamata strukturālais modelis)× | ARIMA (autoregresīvais integrētais slīdošā vidējā) modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1990 | 2015 |
| Autors≠ | Andrew C. Harvey | Box & Jenkins (Box-Jenkins methodology) |
| Tips≠ | State-space (unobserved components) time series model | Univariate time-series model |
| Pirmavots≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 | 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 |
| Citi nosaukumi≠ | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | 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. | 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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