Sammenlign metoder
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| Simpel og dobbelt eksponentiel udjævning (SES / Holt)× | Almindelig mindste kvadraters metode (OLS) regression× | |
|---|---|---|
| Fagområde | Økonometri | Økonometri |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 1957 | 2019 |
| Ophavsperson≠ | Robert G. Brown (SES); Charles C. Holt (linear trend) | Wooldridge (textbook treatment); classical least squares |
| Type≠ | Exponential smoothing forecasting model | Linear regression |
| Oprindelig kilde≠ | Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. link ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Aliasser | SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relaterede≠ | 3 | 5 |
| Resumé≠ | 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. | 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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