Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Model ARIMA (Autoregressive Integrated Moving Average)× | Wygładzanie wykładnicze proste i podwójne (SES / Holt)× | Regresja metodą najmniejszych kwadratów (OLS)× | |
|---|---|---|---|
| Dziedzina | Ekonometria | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model | Regression model |
| Rok powstania≠ | 2015 | 1957 | 2019 |
| Twórca≠ | Box & Jenkins (Box-Jenkins methodology) | Robert G. Brown (SES); Charles C. Holt (linear trend) | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Univariate time-series model | Exponential smoothing forecasting model | Linear regression |
| Źródło pierwotne≠ | 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 | 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 |
| Inne nazwy≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | 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 |
| Pokrewne≠ | 5 | 3 | 5 |
| Podsumowanie≠ | 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). | 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). |
| ScholarGateZbiór danych ↗ |
|
|
|