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Model ARIMA (Autoregresivni integrirani pokretni prosjek)×Jednostavno i dvostruko eksponencijalno izglađivanje (SES / Holt)×
PodručjeEkonometrijaEkonometrija
ObiteljRegression modelRegression model
Godina nastanka20151957
TvoracBox & Jenkins (Box-Jenkins methodology)Robert G. Brown (SES); Charles C. Holt (linear trend)
VrstaUnivariate time-series modelExponential smoothing forecasting model
Temeljni izvorBox, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. link ↗
Drugi naziviBox-Jenkins model, ARIMA(p,d,q), ARIMA ModeliSES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)
Srodne53
SažetakARIMA 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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ScholarGateUsporedite metode: ARIMA · Exponential Smoothing. Preuzeto 2026-06-17 s https://scholargate.app/hr/compare