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| Modello ARIMA (Autoregressive Integrated Moving Average)× | Decomposizione STL: Decomposizione Stagionale-Trend tramite Loess× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia≠ | Regression model | Process / pipeline |
| Anno di origine≠ | 2015 | 1990 |
| Ideatore≠ | Box & Jenkins (Box-Jenkins methodology) | Cleveland, Cleveland, McRae & Terpenning |
| Tipo≠ | Univariate time-series model | nonparametric iterative smoother |
| Fonte seminale≠ | 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 ↗ |
| Alias≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) |
| Correlati≠ | 5 | 3 |
| Sintesi≠ | 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. |
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