Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Lihtne ja kahekordne eksponentsiaalne silumine (SES / Holt)× | ARIMA (autoregressiivne integreeritud liikuv keskmine) mudel× | |
|---|---|---|
| Valdkond | Ökonomeetria | Ökonomeetria |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 1957 | 2015 |
| Looja≠ | Robert G. Brown (SES); Charles C. Holt (linear trend) | Box & Jenkins (Box-Jenkins methodology) |
| Tüüp≠ | Exponential smoothing forecasting model | Univariate time-series model |
| Algallikas≠ | Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. link ↗ | 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 |
| Rööpnimetused≠ | SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Seotud≠ | 3 | 5 |
| Kokkuvõte≠ | 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. | 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). |
| ScholarGateAndmestik ↗ |
|
|