เปรียบเทียบวิธี

	แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)	แบบจำลองหน่วยความจำยาว (ARFIMA, FIGARCH)
สาขาวิชา	เศรษฐมิติ	การเงิน
ตระกูล	Regression model	Regression model
ปีกำเนิด	2015	1980
ผู้ริเริ่ม	Box & Jenkins (Box-Jenkins methodology)	Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)
ประเภท	Univariate time-series model	Fractionally integrated time series model
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	ARFIMA, FIGARCH, fractionally integrated models, fractional integration
ที่เกี่ยวข้อง	5	4
สรุป	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.