Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| STL Decomposition× | Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство≠ | Process / pipeline | Regression model |
| Год появления≠ | 1990 | 2015 |
| Автор метода≠ | Cleveland, Cleveland, McRae & Terpenning | Box & Jenkins (Box-Jenkins methodology) |
| Тип≠ | nonparametric iterative smoother | Univariate time-series model |
| Основополагающий источник≠ | Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–73. 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 |
| Другие названия≠ | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Связанные≠ | 3 | 5 |
| Сводка≠ | STL Decomposition, introduced by Cleveland, Cleveland, McRae, and Terpenning (1990), is a nonparametric procedure that separates a time series into three additive components — trend, seasonal, and remainder — using iterative locally weighted regression (loess). Widely used in economics, meteorology, and data science, it handles time series of any periodicity and is robust to the presence of outliers, making it a highly flexible alternative to classical decomposition methods. | 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). |
| ScholarGateНабор данных ↗ |
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