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× | Minste kvadraters metode (OLS)× | |
|---|---|---|---|
| Fagfelt | Økonometri | Økonometri | Økonometri |
| Familie | Regression model | Regression model | Regression model |
| Opprinnelsesår≠ | 2015 | 1960 | 2019 |
| Opphavsperson≠ | Box & Jenkins (Box-Jenkins methodology) | Charles C. Holt and Peter R. Winters | Wooldridge (textbook treatment); classical least squares |
| Type≠ | Univariate time-series model | Exponential smoothing forecasting model | Linear regression |
| 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relaterte≠ | 5 | 4 | 5 |
| 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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