Comparer des méthodes

	Modèle ARIMA (Autoregressive Integrated Moving Average)	Modèles à mémoire longue (ARFIMA, FIGARCH)
Domaine	Économétrie	Finance
Famille	Regression model	Regression model
Année d'origine	2015	1980
Auteur d'origine	Box & Jenkins (Box-Jenkins methodology)	Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)
Type	Univariate time-series model	Fractionally integrated time series model
Source fondatrice	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	Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29.
Alias	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	ARFIMA, FIGARCH, fractionally integrated models, fractional integration
Apparentées	5	4
Résumé	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).	Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration.