Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Простое и двойное экспоненциальное сглаживание (SES / Холт)× | Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | Модель пространства состояний (фильтр Калмана)× | |
|---|---|---|---|
| Область | Эконометрика | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model | Regression model |
| Год появления≠ | 1957 | 2015 | 1990 |
| Автор метода≠ | Robert G. Brown (SES); Charles C. Holt (linear trend) | Box & Jenkins (Box-Jenkins methodology) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Тип≠ | Exponential smoothing forecasting model | Univariate time-series model | State space time series model |
| Основополагающий источник≠ | Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. link ↗ | 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 | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Другие названия≠ | SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Связанные≠ | 3 | 5 | 4 |
| 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. | 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). | A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases. |
| ScholarGateНабор данных ↗ |
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