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| Αποσύνθεση STL: Αποσύνθεση Εποχικότητας-Τάσης με χρήση Loess× | Μοντέλο ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Πεδίο | Οικονομετρία | Οικονομετρία |
| Οικογένεια≠ | Process / pipeline | Regression model |
| Έτος προέλευσης≠ | 1990 | 2015 |
| Δημιουργός≠ | Cleveland, Cleveland, McRae & Terpenning | Box & Jenkins (Box-Jenkins methodology) |
| Τύπος≠ | nonparametric iterative smoother | Univariate time-series model |
| Θεμελιώδης πηγή≠ | 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 |
| Εναλλακτικές ονομασίες≠ | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Συναφείς≠ | 3 | 5 |
| Σύνοψη≠ | 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). |
| ScholarGateΣύνολο δεδομένων ↗ |
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