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| Modello ARIMA (Autoregressive Integrated Moving Average)× | Modello Strutturale di Serie Storiche (Modello Strutturale di Base)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2015 | 1990 |
| Ideatore≠ | Box & Jenkins (Box-Jenkins methodology) | Andrew C. Harvey |
| Tipo≠ | Univariate time-series model | State-space (unobserved components) time series model |
| Fonte seminale≠ | 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 | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 |
| Alias≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | 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). | 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. |
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