Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| ARIMA (Autoregressive Integrated Moving Average) Modell× | Holt-Winters trippel eksponentiell glatting× | |
|---|---|---|
| Fagfelt | Økonometri | Økonometri |
| Familie | Regression model | Regression model |
| Opprinnelsesår≠ | 2015 | 1960 |
| Opphavsperson≠ | Box & Jenkins (Box-Jenkins methodology) | Charles C. Holt and Peter R. Winters |
| Type≠ | Univariate time-series model | Exponential smoothing forecasting model |
| Opprinnelig kilde≠ | 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 | Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342. DOI ↗ |
| Alias≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme |
| Relaterte≠ | 5 | 4 |
| Sammendrag≠ | 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). | Holt-Winters triple exponential smoothing is a forecasting model that extends Holt's double smoothing by adding a seasonal component, introduced by Peter Winters in 1960 building on Charles Holt's work. It tracks three evolving quantities — level, trend, and season — and combines them to forecast a continuous time series. |
| ScholarGateDatasett ↗ |
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