Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Сезонен ARIMA (SARIMA)× | STL разлагане: Разлагане на сезонност и тренд чрез Loess× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство≠ | Regression model | Process / pipeline |
| Година на възникване≠ | 2015 | 1990 |
| Създател≠ | Box & Jenkins (seasonal extension of ARIMA) | Cleveland, Cleveland, McRae & Terpenning |
| Тип≠ | Seasonal time-series model | nonparametric iterative smoother |
| Основополагащ източник≠ | 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 | 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 ↗ |
| Други названия≠ | seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) |
| Свързани≠ | 5 | 3 |
| Резюме≠ | 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. | 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. |
| ScholarGateНабор от данни ↗ |
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