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| Модел ARIMA (Autoregressive Integrated Moving Average)× | 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 (Box-Jenkins methodology) | Cleveland, Cleveland, McRae & Terpenning |
| Тип≠ | Univariate 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≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) |
| Свързани≠ | 5 | 3 |
| Резюме≠ | 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). | 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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