Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Просте та подвійне експоненційне згладжування (SES / Хольт)× | Регресія звичайно найменших квадратів (ЗНК)× | Сезонна ARIMA (SARIMA)× | |
|---|---|---|---|
| Галузь | Економетрика | Економетрика | Економетрика |
| Родина | Regression model | Regression model | Regression model |
| Рік появи≠ | 1957 | 2019 | 2015 |
| Автор методу≠ | Robert G. Brown (SES); Charles C. Holt (linear trend) | Wooldridge (textbook treatment); classical least squares | Box & Jenkins (seasonal extension of ARIMA) |
| Тип≠ | Exponential smoothing forecasting model | Linear regression | Seasonal time-series model |
| Основоположне джерело≠ | 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 | 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 |
| Інші назви≠ | 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 | seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA |
| Пов'язані≠ | 3 | 5 | 5 |
| Підсумок≠ | 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). | SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period. |
| ScholarGateНабір даних ↗ |
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