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| Modello ARIMA (Autoregressive Integrated Moving Average)× | Smorzamento Esponenziale Semplice e Doppio (SES / Holt)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2015 | 1957 |
| Ideatore≠ | Box & Jenkins (Box-Jenkins methodology) | Robert G. Brown (SES); Charles C. Holt (linear trend) |
| Tipo≠ | Univariate time-series model | Exponential smoothing forecasting 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 | Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. link ↗ |
| Alias≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt) |
| Correlati≠ | 5 | 3 |
| 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). | Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta. |
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