Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Μοντέλο Κινητού Μέσου Όρου (MA)× | Μοντέλο ARMA (Αυτοπαλινδρομικής Κινητού Μέσου)× | |
|---|---|---|
| Πεδίο | Οικονομετρία | Οικονομετρία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης | 1970 | 1970 |
| Δημιουργός≠ | Box and Jenkins | George E. P. Box and Gwilym M. Jenkins |
| Τύπος≠ | Linear time series model | Time series model |
| Θεμελιώδης πηγή≠ | Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Εναλλακτικές ονομασίες | MA model, MA(q) process, moving-average process, Box-Jenkins MA | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Συναφείς | 5 | 5 |
| Σύνοψη≠ | The Moving Average model of order q — written MA(q) — expresses the current value of a time series as a linear combination of the current and past random shocks (innovations). Unlike the AR model which uses lagged values of the series itself, the MA model uses lagged error terms, making it well-suited for capturing short-lived disturbances that dissipate over q periods. | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. |
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