Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Простое и двойное экспоненциальное сглаживание (SES / Холт)× | Регрессия методом обыкновенных наименьших квадратов (ОНМК)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1957 | 2019 |
| Автор метода≠ | Robert G. Brown (SES); Charles C. Holt (linear trend) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Exponential smoothing forecasting model | Linear regression |
| Основополагающий источник≠ | 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 |
| Другие названия | 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 |
| Связанные≠ | 3 | 5 |
| Сводка≠ | 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). |
| ScholarGateНабор данных ↗ |
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