Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Μοντέλο Μη Γραμμικής Αυτοπαλίνδρομης Συσχέτισης (NAR)× | Μοντέλο ARIMA (Αυτοπαλινδρομικό Ολοκληρωμένο Κινητό Μέσος Όρος)× | |
|---|---|---|
| Πεδίο | Οικονομετρία | Οικονομετρία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 1978-1990 | 1970 |
| Δημιουργός≠ | Tong, H. (threshold AR); Terasvirta, T. (STAR variant) | George Box and Gwilym Jenkins |
| Τύπος≠ | Nonlinear time series model | Time series forecasting model |
| Θεμελιώδης πηγή≠ | Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522201 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Εναλλακτικές ονομασίες | NAR model, nonlinear autoregression, NLAR, threshold autoregressive model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Συναφείς | 6 | 6 |
| Σύνοψη≠ | The Nonlinear AR model extends the classical autoregressive framework by allowing the mapping from past values to the current value to follow an arbitrary or regime-switching nonlinear function. Major families include the Self-Exciting Threshold AR (SETAR), Smooth Transition AR (STAR), and neural network AR, each capturing different forms of asymmetry, regime shifts, or smooth nonlinear dynamics in univariate time series. | The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
| ScholarGateΣύνολο δεδομένων ↗ |
|
|